Image processing apparatus, image processing method, and computer-readable recording medium

ABSTRACT

An image processing apparatus includes: a pixel value group creation unit configured to create, for a plurality of pieces of image data generated by an image sensor including: a plurality of pixels arranged two-dimensionally to receive light from outside and generate a signal corresponding to an amount of the received light; and a plurality of read-out circuits shared by a predetermined number of pixels and configured to read out the signal as pixel values, a plurality of pixel value groups by classifying the pixel values for each of the plurality of read-out circuits; and a noise determination unit configured to determine whether blinking defect noise occurs in each of the plurality of pixel value groups, based on distribution of the pixel values of each of the plurality of pixel value groups created by the pixel value group creation unit.

CROSS REFERENCES TO RELATED APPLICATIONS

This application is a continuation of PCT international application Ser.No. PCT/JP2015/051422, filed on Jan. 20, 2015 which designates theUnited States, incorporated herein by reference.

BACKGROUND 1. Technical Field

The disclosure relates to an image processing apparatus, an imageprocessing method, and a computer-readable recording medium fordetermining blinking defect noise that varies within a fixed range, suchas RTS noise occurred in an image sensor including a plurality of pixelsthat is two-dimensionally arranged.

2. Related Art

In recent years, in an image sensor such as a complementary metal oxidesemiconductor (CMOS), miniaturization of a pixel and a read-out circuitthat reads out a signal from the pixel has been progressing. In thiscontext of miniaturization, there is a problem of lowered sensitivityand increased noise. To cope with lowered sensitivity, by employing asharing pixel structure of reading out a signal with one read-outcircuit shared by a plurality of pixels, it is possible to reduce thearea of the read-out circuit and enhance the aperture ratio (proportionof light receiving portion) of each of the pixels, thereby enhancing thesensitivity.

Noise that occurs in the image sensor includes dark current shot noisedue to dark current and random noise attributed to thermal noise on theread-out circuits. Moreover, there is not only the dark current shotnoise and the random noise but also a defective pixel on which a pixelvalue constantly indicates an abnormal value, blinking defect noise thatcauses the pixel value to vary in individual imaging, or the like. Theblinking defect noise includes a random telegraph signal (RTS) noiseattributed to the read-out circuit. As a technique of detecting the RTSnoise for each of pixels of the image sensors, there is a knowntechnique of calculating a value by subtracting an average value ofpixel values on a plurality of images shot from the pixel value of eachof the images, and in case where the value is a shot noise or above,determining the value as a noise level of the RTS noise (hereinafter,referred to as “RTS noise level”) (refer to JP 2012-105063 A).

SUMMARY

In some embodiments, an image processing apparatus includes: a pixelvalue group creation unit configured to create, for a plurality ofpieces of image data generated by an image sensor including: a pluralityof pixels arranged two-dimensionally to receive light from outside andgenerate a signal corresponding to an amount of the received light; anda plurality of read-out circuits shared by a predetermined number ofpixels and configured to read out the signal as pixel values, aplurality of pixel value groups by classifying the pixel values for eachof the plurality of read-out circuits; and a noise determination unitconfigured to determine whether blinking defect noise occurs in each ofthe plurality of pixel value groups, based on distribution of the pixelvalues of each of the plurality of pixel value groups created by thepixel value group creation unit.

In some embodiments, An image processing method includes: creating, fora plurality of pieces of image data generated by an image sensorincluding: a plurality of pixels arranged two-dimensionally to receivelight from outside and generate a signal corresponding to an amount ofthe received light; and a plurality of read-out circuits shared by apredetermined number of pixels and configured to read out the signal aspixel values, a plurality of pixel value groups by classifying the pixelvalues for each of the plurality of read-out circuits; and determiningwhether blinking defect noise occurs in each of the plurality of pixelvalue groups, based on distribution of the pixel values of each of theplurality of pixel value groups.

In some embodiments, provided is a non-transitory computer-readablerecording medium with an executable program stored thereon. The programcauses an image processing apparatus to execute: creating, for aplurality of pieces of image data generated by an image sensorincluding: a plurality of pixels arranged two-dimensionally to receivelight from outside and generate a signal corresponding to an amount ofthe received light; and a plurality of read-out circuits shared by apredetermined number of pixels and configured to read out the signal aspixel values, a plurality of pixel value groups by classifying the pixelvalues for each of the plurality of read-out circuits; and determiningwhether blinking defect noise occurs in each of the plurality of pixelvalue groups, based on distribution of the pixel values of each of theplurality of pixel value groups.

The above and other features, advantages and technical and industrialsignificance of this invention will be better understood by reading thefollowing detailed description of presently preferred embodiments of theinvention, when considered in connection with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram schematically illustrating a configuration ofan imaging system according to a first embodiment of the presentinvention;

FIG. 2 is a diagram schematically illustrating a configuration of mainportions of an image sensor according to the first embodiment of thepresent invention;

FIG. 3 is a diagram illustrating variation of amplifier output that isoutput from an amplifier unit at the time of occurrence of RTS noise ina case where light is shielded so as to avoid the light from reachingthe image sensor according to the first embodiment of the presentinvention;

FIG. 4 is a diagram illustrating distribution of pixel values read outby using the amplifier unit in which RTS noise is occurring;

FIG. 5 is a flowchart illustrating outline of processing executed by afirst image processing apparatus according to the first embodiment ofthe present invention;

FIG. 6 is a flowchart illustrating an outline of isolated pointdetection and pixel value group creation processing in FIG. 5;

FIG. 7 is a diagram schematically illustrating a pixel of an imagesensor that outputs a pixel value classified into a same pixel valuegroup;

FIG. 8 is a flowchart illustrating an outline of average random noiseamount calculation and RTS flag setting processing in FIG. 5;

FIG. 9 is a flowchart illustrating an outline of processing in the RTSflag setting processing in FIG. 8;

FIG. 10 is a flowchart illustrating an outline of RTS feature datacalculation processing in FIG. 5;

FIG. 11 is a diagram illustrating an exemplary absolute value histogram;

FIG. 12 is a flowchart illustrating an outline of RTS noisedetermination processing in FIG. 5;

FIG. 13 is a flowchart illustrating outline of processing executed by asecond image processing apparatus according to the first embodiment ofthe present invention;

FIG. 14 is a flowchart illustrating an outline of representative valuecalculation processing in FIG. 13;

FIG. 15 is a flowchart illustrating an exemplary random noise model;

FIG. 16 is a flowchart illustrating an outline of correction valuecalculation processing in FIG. 13;

FIG. 17 is a diagram illustrating exemplary mixed normal distributionapproximation of an absolute value histogram;

FIG. 18 is a diagram illustrating a relationship between RTS_Valueluminance distribution and a candidate value, in Candidate ValueCalculation Method 2 according to Second Modification of the firstembodiment of the present invention;

FIG. 19 is a diagram illustrating a relationship between RTS_Valuehistogram and a candidate value, in Candidate Value Calculation Method 3according to Third Modification of the first embodiment of the presentinvention;

FIG. 20 is a flowchart illustrating an outline of RTS noisedetermination processing executed by a first image processing apparatusaccording to a second embodiment of the present invention;

FIG. 21 is a flowchart illustrating an outline of processing executed bythe second image processing apparatus according to a second embodimentof the present invention;

FIG. 22 is a block diagram schematically illustrating a configuration ofan imaging system according to a third embodiment of the presentinvention;

FIG. 23 is a flowchart illustrating an outline of processing executed bythe imaging system according to the third embodiment of the presentinvention;

FIG. 24 is a flowchart illustrating an outline of setting processing inFIG. 23; and

FIG. 25 is a flowchart illustrating an outline of image processing inFIG. 23.

DETAILED DESCRIPTION

Exemplary embodiments of the present invention will be described belowwith reference to the drawings. The present invention is not limited bythe embodiments described below. The same reference signs are used todesignate the same elements throughout the drawings.

First Embodiment

Configuration of Imaging System

FIG. 1 is a block diagram schematically illustrating a configuration ofan imaging system according to a first embodiment of the presentinvention. An imaging system 1 illustrated in FIG. 1 includes an imagingapparatus 10, a first image processing apparatus 20, a second imageprocessing apparatus 30, and a display device 40.

Configuration of Imaging Apparatus

First, a configuration of the imaging apparatus 10 will be described. Asillustrated in FIG. 1, the imaging apparatus 10 includes an opticalsystem 101, a diaphragm 102, a shutter 103, a driver 104, an imagesensor 105, an analog processing unit 106, an A/D converter 107, anoperating unit 108, a memory I/F unit 109, a recording medium 110, avolatile memory 111, a non-volatile memory 112, a bus 113, an imagingcontroller 114, and a first external I/F unit 115.

The optical system 101 includes a plurality of lenses. The opticalsystem 101 includes a focus lens and a zoom lens, for example.

The diaphragm 102 adjusts exposure by limiting an incident amount oflight collected by the optical system 101. Under the control of theimaging controller 114, the diaphragm 102 limits the incident amount ofthe light collected by the optical system 101.

The shutter 103 sets the state of the image sensor 105 to an exposurestate or a light-shielding state. The shutter 103 includes a focal planeshutter, for example.

Under the control of the imaging controller 114 described below, thedriver 104 drives the optical system 101, the diaphragm 102, and theshutter 103. For example, the driver 104 performs zoom magnificationchange or focusing position adjustment for the imaging apparatus 10 bymoving the optical system 101 along an optical axis O1.

Under the control of the imaging controller 114 described below, theimage sensor 105 receives the light collected by the optical system 101,converts the received light into image data (electric signal), andoutputs the image data. The image sensor 105 includes a complementarymetal oxide semiconductor (CMOS) including a plurality oftwo-dimensionally arranged pixels. An RGB filter arranged in a Bayerarray is disposed on a front surface of each of the pixels. The imagesensor 105 is not limited to the Bayer array but may be in a stackedform such as Foveon. Moreover, it is possible to apply not only RGBfilter but also any other filter such as a complementary color filter.Alternatively, it may be possible to separately dispose a light sourcecapable of emitting light beams of different colors on a time divisionbasis without disposing a filter on the image sensor 105, and to form acolor image using sequentially captured images while changing the colorsof the beams to be emitted.

Now, the configuration of the image sensor 105 will be described indetail. FIG. 2 is a diagram schematically illustrating the configurationof main portions of the image sensor 105. The image sensor 105 in FIG. 2is an exemplary case where a read-out circuit is shared by a pluralityof pixels in order to enhance sensitivity by increasing the apertureratio of the pixel. The image sensor 105 illustrated in FIG. 2 includesone read-out circuit arranged for eight pixels, that is, two pixels inthe horizontal direction (lateral direction)×four pixels in the verticaldirection (longitudinal direction). While FIG. 2 illustrates anexemplary case where one read-out circuit is arranged as a group foreight pixels, that is, two pixels in the horizontal direction (lateraldirection) x four pixels in the vertical direction (longitudinaldirection), the image sensor 105 according to the first embodiment has aconfiguration in which the above-described pixels and the read-outcircuit are arranged side by side in horizontal and vertical directions.

As illustrated in FIG. 2, the image sensor 105 includes a plurality ofpixels (photodiodes) 105 a, a first switch 105 b, vertical transfer line105 c, a floating diffusion (FD) unit 105 d, an amplifier unit 105 e, asecond switch 105 f, a control line 105 g, and a transfer line 105 h.Each of the plurality of pixels 105 a receives light by exposure,performs photoelectric conversion, thereby generating electrical chargesin accordance with an exposure amount. The first switch 105 b isprovided on each of the plurality of pixels 105 a, and opens or closesin accordance with the control of the imaging controller 114. Thevertical transfer line 105 c transfers signals (electrical charges)output from each of the plurality of pixels 105 a in the verticaldirection. The floating diffusion (FD) unit 105 d stores the signalsoutput from the plurality of pixels 105 a. The amplifier unit 105 eamplifies the signals output from the FD unit 105 d. The second switch105 f opens or closes according to the control of the imaging controller114. The control line 105 g controls the second switch 105 f. Thetransfer line 105 h transfers the electrical signal amplified by theamplifier unit 105 e.

In a case where the above-configured image sensor 105 reads out a signalthat corresponds to the exposure amount on pixels 105 a(1) to 105 a(8)as a pixel value, the electrical charge generated on the pixel 105 a(1)is transferred to the FD unit 105 d by first resetting the FD unit 105 dand by turning on solely a first switch 105 b(1) by the imagingcontroller 114. Thereafter, the imaging controller 114 turns on thesecond switch 105 f, whereby the image sensor 105 causes the amplifierunit 105 e to amplify the electrical charges stored in the FD unit 105 dand reads out (outputs) the electrical charge as a pixel value. Next,the image sensor 105 resets the FD unit 105 d and the imaging controller114 turns on solely a first switch 105 b(2), whereby the image sensor105 transfers the electrical charges generated on the pixel 105 a(2) tothe FD unit 105 d. Thereafter, the imaging controller 114 turns on thesecond switch 105 f, whereby the image sensor 105 causes the amplifierunit 105 e to amplify the electrical charges stored in the FD unit 105 dand reads out the electrical charges as a pixel value. By sequentiallyperforming such read-out operation, the image sensor 105 cansequentially output the signal that corresponds to the exposure amounton each of the pixels 105 a(1) to 105 a(8), as a pixel value. In thefirst embodiment, the amplifier unit 105 e functions as a read-outcircuit that reads out electrical charges from each of the plurality ofpixels 105 a.

Returning to FIG. 1, description of the configuration of the imagingapparatus 10 follows below.

The analog processing unit 106 performs predetermined analog processingonto an analog signal input from the image sensor 105 and outputs theprocessed signal to the A/D converter 107. Specifically, the analogprocessing unit 106 performs noise reduction processing, gain-upprocessing, or the like, on the analog signal input from the imagesensor 105. For example, the analog processing unit 106 performs, ontothe analog signal, reduction of reset noise, etc. and waveform shaping,and then, further performs gain-up so as to achieve intended brightness.

The A/D converter 107 generates digital image data (hereinafter,referred to as “RAW image data”) by performing A/D conversion onto theanalog signal input from the analog processing unit 106, and outputs thegenerated data to the volatile memory 111 via the bus 113. The A/Dconverter 107 may directly output the RAW image data to each of portionsof the imaging apparatus 10 described below. It may be possible toconfigure such that the analog processing unit 106 and the A/D converter107 are provided on the image sensor 105, and that the image sensor 105directly outputs digital RAW image data.

The operating unit 108 provides various commands to the imagingapparatus 10. Specifically, the operating unit 108 includes a powerswitch, a release switch, an operation switch, and a moving imageswitch. The power switch switches the power supply states of the imagingapparatus 10 between an on-state and an off-state. The release switchprovides an instruction of still image shooting. The operation switchswitches various settings of the imaging apparatus 10. The moving imageswitch provides an instruction of moving image shooting.

The recording medium 110 includes a memory card attached from outside ofthe imaging apparatus 10, and is removably attached onto the imagingapparatus 10 via the memory I/F unit 109. Moreover, the recording medium110 may output programs and various types of information to thenon-volatile memory 112 via the memory I/F unit 109 under the control ofthe imaging controller 114.

The volatile memory 111 temporarily stores image data input from the A/Dconverter 107 via the bus 113. For example, the volatile memory 111temporarily stores image data sequentially output from the image sensor105 frame by frame, via the analog processing unit 106, the A/Dconverter 107, and the bus 113. The volatile memory 111 includes asynchronous dynamic random access memory (SDRAM).

The non-volatile memory 112 records various programs needed to operatethe imaging apparatus 10 and various types of data used in execution ofthe program. Moreover, the non-volatile memory 112 includes a programrecording unit 112 a, an RTS noise information recording unit 112 b, anda random noise model information recording unit 112 c. The RTS noiseinformation recording unit 112 b records RTS noise positionalinformation of the RTS noise on the image sensor 105, input via thefirst external I/F unit 115. The random noise model informationrecording unit 112 c records a random noise model. The non-volatilememory 112 includes a flash memory.

The bus 113 includes a transmission line that connects individualelements of the imaging apparatus 10 with each other, and transfersvarious types of data generated inside the imaging apparatus 10 to eachof the individual elements of the imaging apparatus 10.

The imaging controller 114 includes a central processing unit, andintegrally controls operation of the imaging apparatus 10 by providinginstruction and transferring data to individual elements of the imagingapparatus 10 in response to an instruction signal and a release signalfrom the operating unit 108. For example, in a case where a secondrelease signal has been input from the operating unit 108, the imagingcontroller 114 performs control of starting shooting operation on theimaging apparatus 10. Herein, the shooting operation on the imagingapparatus 10 is operation of predetermined processing performed by theanalog processing unit 106 and the A/D converter 107, onto the imagedata output by the image sensor 105. The image data processed in thismanner are recorded in the recording medium 110 via the bus 113 and thememory I/F unit 109 under the control of the imaging controller 114.

The first external I/F unit 115 outputs information input from externalapparatuses via the bus 113, to the non-volatile memory 112 or thevolatile memory 111, and outputs, to external apparatuses via the bus113, information recorded in the volatile memory 111, information storedin the non-volatile memory 112, and the image data generated by theimage sensor 105. Specifically, the first external I/F unit 115 outputsthe image data generated by the image sensor 105 to the first imageprocessing apparatus 20 and the second image processing apparatus 30 viathe bus 113.

Configuration of First Image Processing Apparatus

Next, the configuration of the first image processing apparatus 20 willbe described. The first image processing apparatus 20 includes a secondexternal I/F unit 201, and an RTS noise detection unit 202.

The second external I/F unit 201 obtains the image data generated by theimage sensor 105, via the first external I/F unit 115 of the imagingapparatus 10, and outputs the obtained image data to the RTS noisedetection unit 202. Moreover, the second external I/F unit 201 outputsthe information input from the RTS noise detection unit 202, to theimaging apparatus 10.

The RTS noise detection unit 202 detects RTS noise that occurs on theimage sensor 105 based on the RAW image captured by the imagingapparatus 10, and records a result of the detection in the non-volatilememory 112 via the second external I/F unit 201, the first external I/Funit 115, and the bus 113. The RTS noise detection unit 202 includes anisolated point detection unit 202 a, a pixel value group creation unit202 b, an average random noise amount calculation unit 202 c, an RTSnoise feature data calculation unit 202 d, and an RTS noisedetermination unit 202 e.

Based on a dark-time RAW image (hereinafter, referred to simply as the“dark-time RAW image) that corresponds to a plurality of pieces of RAWimage data captured in a state where the image sensor 105 islight-shielded (for example, in a state where the shutter 103 is closed;hereinafter, referred to as a “dark-time state”), the isolated pointdetection unit 202 a performs detection of an isolated point andsubtraction of OB components, then, outputs a result of isolationdetection and the dark-time RAW image after subtraction, to the pixelvalue group creation unit 202 b. In the first embodiment, the isolatedpoint detection unit 202 a functions as a defective pixel detectionunit.

The pixel value group creation unit 202 b excludes a pixel value of thepixel detected as an isolated point on the basis a plurality of pixelvalues of the dark-time RAW images after subtraction by the isolatedpoint detection unit 202 a. Further, the pixel value group creation unit202 b creates a plurality of pixel value groups by performingclassification for each of the read-out circuits (amplifier units 105 e)based on the sharing pixel system of the image sensor 105, and outputsthe plurality of pixel value groups to the average random noise amountcalculation unit 202 c. Alternatively, the pixel value group creationunit 202 b may use the pixel value corrected in accordance with thedetected isolated point, without excluding the pixel value of the pixeldetected as an isolated point by the isolated point detection unit 202a.

The average random noise amount calculation unit 202 c calculates arandom noise amount of each of the plurality of pixel value groupscreated by the pixel value group creation unit 202 b, calculates arandom noise amount of each of the plurality of dark-time RAW images,and outputs individual calculated random noise amounts to the RTS noisefeature data calculation unit 202 d. Furthermore, using the random noiseamounts calculated as described above, the average random noise amountcalculation unit 202 c calculates an average random noise amount, thatis, an average of random noise amounts of all the pixel value groupsbased on identification of a pixel value group having a possibility ofoccurrence of RTS noise, and based on the random noise amount of theother pixel value groups. In the first embodiment, the average randomnoise amount calculation unit 202 c functions as a random noise amountcalculation unit.

The RTS noise feature data calculation unit 202 d calculates RTS noisefeature data, that is, the feature data representing the RTS noise leveland a frequency of occurrence, for the pixel value group having apossibility of occurrence of RTS noise, and outputs the calculated RTSnoise feature data to the RTS noise determination unit 202 e. Followingthe distribution of the pixel value of the pixel value group on whichRTS noise occurs, as illustrated in FIG. 4, the RTS noise feature datacalculation unit 202 d calculates RTS noise feature data using fittingprocessing described below, or the like. The RTS noise feature datacalculation unit 202 d functions as a feature data calculation unit, inthe present embodiment.

Based on distribution of individual pixel values of the plurality ofgroups created by the pixel value group creation unit 202 b, the RTSnoise determination unit 202 e determines whether RTS noise occurs asblinking defect noise, for each of the plurality of pixel value groups.Specifically, based on the RTS noise feature data calculated by the RTSnoise feature data calculation unit 202 d for the pixel value grouphaving a possibility of occurrence of RTS noise, the RTS noisedetermination unit 202 e determines (detects) whether RTS noise of alevel that needs correction occurs, for the pixels of the image sensor105 constituting corresponding pixel group (pixels of the image sensor105 that share the same amplifier unit 105 e (read-out circuit). In acase where the RTS noise is occurring, the RTS noise determination unit202 e records RTS noise information in the non-volatile memory 112 ofthe imaging apparatus 10. The RTS noise information is information inwhich an RTS noise detection result, positional information of theread-out circuit (amplifier unit 105 e) of the corresponding imagesensor 105 (that is, positional information of a sharing pixel blockformed solely with the pixels that share the read-out circuit), and thefeature data calculated by the RTS noise feature data calculation unit202 d, are associated with each other. In the present embodiment, theRTS noise determination unit 202 e functions as a noise determinationunit.

Configuration of Second Image Processing Apparatus

Next, the configuration of the second image processing apparatus 30 willbe described. The second image processing apparatus 30 includes a thirdexternal I/F unit 301, an RTS noise correction unit 302, and an imageprocessing unit 303.

The third external I/F unit 301 obtains the image data generated by theimage sensor 105 and RTS noise information related to the RTS noiseoutput from the non-volatile memory 112, via the first external I/F unit115 of the imaging apparatus 10, and outputs the obtained image data andthe RTS noise information to the RTS noise correction unit 302.

The RTS noise correction unit 302 performs RTS noise correctionprocessing of correcting RTS noise onto the RAW image recorded in theRTS noise information recording unit 112 b of the non-volatile memory112 of the imaging apparatus 10, and outputs the corrected RAW image tothe image processing unit 303. The RTS noise correction unit 302includes an RTS noise pixel determination unit 302 a, a candidate valuecalculation unit 302 b, a representative value calculation unit 302 c, arandom noise amount estimation unit 302 d, and a correction valuecalculation unit 302 e.

The RTS noise pixel determination unit 302 a obtains the RTS noiseinformation recorded in the RTS noise information recording unit 112 bof the imaging apparatus 10, via the third external I/F unit 301, thefirst external I/F unit 115, and the bus 113, determines whether RTSnoise occurs on the pixel of the RAW image that has been obtained, andoutputs a determination result to the candidate value calculation unit302 b and the representative value calculation unit 302 c. Specifically,when the pixel position is input into the RTS noise pixel determinationunit 302 a, the RTS noise pixel determination unit 302 a determineswhether the RTS information that corresponds to the pixel is recorded onthe RTS noise information recording unit 112 b of the imaging apparatus10. In a case where the information is recorded, the RTS noise pixeldetermination unit 302 a outputs the RTS noise information (informationindicating whether the RTS noise is present). In a case where theinformation is not recorded on the RTS noise information recording unit112 b of the imaging apparatus 10, the RTS noise pixel determinationunit 302 a determines the pixel to be a pixel free from occurrence ofthe RTS noise and does not output the RTS noise information.

In a case where the RTS noise pixel determination unit 302 a determinesthat RTS noise occurs on the pixel of interest, the candidate valuecalculation unit 302 b calculates a plurality of candidate values for acorrection amount that corresponds to the pixel value of the pixel ofinterest, based on the pixel value of the pixel of interest on the RAWimage, and the determination result from the RTS noise pixeldetermination unit 302 a, and then, outputs the pixel value of the pixelof interest on the RAW image and the plurality of calculated candidatevalues, to each of the representative value calculation unit 302 c, therandom noise amount estimation unit 302 d, and the correction valuecalculation unit 302 e.

In a case where the RTS noise pixel determination unit 302 a hasdetermined that RTS noise occurs on the pixel of interest, therepresentative value calculation unit 302 c calculates a representativevalue that corresponds to the pixel value for the case of no occurrenceof RTS noise, based on the pixel that at least has been determined to befree from RTS noise by the RTS noise pixel determination unit 302 aamong the pixels around the pixel of interest, and based on a referencevalue of the random noise amount that corresponds to the pixel ofinterest, calculated by the random noise amount estimation unit 302 ddescribed below. The representative value calculation unit 302 c outputsthe pixel value of the pixel of interest on the RAW image, the pluralityof candidate values, and the above-calculated representative value, tothe correction value calculation unit 302 e.

The random noise amount estimation unit 302 d estimates the random noiseamount that corresponds to the pixel value based on the random noisemodel recorded in the random noise model information recording unit 112c of the imaging apparatus 10, and outputs an estimation result to therepresentative value calculation unit 302 c. That is, when a pixel valueis input into the random noise amount estimation unit 302 d, a randomnoise amount that corresponds to the pixel value is output.

In a case where the RTS noise pixel determination unit 302 a hasdetermined that the pixel is a pixel having a possibility of occurrenceof RTS noise on the pixel of interest, the correction value calculationunit 302 e corrects the pixel value of the pixel of interest based onthe plurality of candidate values calculated by the candidate valuecalculation unit 302 b. Specifically, based on the pixel value of thepixel of interest on the RAW image, the plurality of candidate valuescalculated by the candidate value calculation unit 302 b, and therepresentative value calculated by the representative value calculationunit 302 c, the correction value calculation unit 302 e calculates apixel value for which the RTS noise has been corrected, and outputs thepixel value to the image processing unit 303. More specifically, thecorrection value calculation unit 302 e corrects the pixel value of thepixel of interest based on the candidate value that causes a correctionresult to come closest to the representative value calculated by therepresentative value calculation unit 302 c, among the plurality ofcandidate values calculated by the candidate value calculation unit 302b. In contrast, in a case where the RTS noise pixel determination unit302 a has determined that the pixel is a pixel on which RTS noise doesnot occur on the pixel of interest, the correction value calculationunit 302 e outputs the pixel value of the pixel of interest on the RAWimage, without any change.

The image processing unit 303 performs predetermined image processingonto the image data for which RTS noise has been corrected by the RTSnoise correction unit 302, and outputs the processed data to the displaydevice 40. The predetermined image processing herein corresponds tobasic image processing including at least optical black subtractionprocessing, white balance adjustment processing, and includingsynchronization processing of the image data, color matrix calculationprocessing, y correction processing, color reproduction processing, edgeenhancement processing, and noise reduction processing, in a case wherethe image sensor is arranged in a Bayer array. Moreover, the imageprocessing unit 303 performs image processing of reproducing a naturalimage based on individual image processing parameters that have been setbeforehand. The parameters of image processing are values of contrast,sharpness, saturation, white balance, and gradation.

Configuration of Display Device

Next, the configuration of the display device 40 will be described. Thedisplay device 40 displays an image that corresponds to the image datainput from the second image processing apparatus 30. The display device40 includes a display panel of liquid crystal, organicelectroluminescence (EL), or the like.

On the above-configured imaging system 1, the first image processingapparatus 20 detects RTS noise that occurs on the image sensor 105, thesecond image processing apparatus 30 corrects the RTS noise detected bythe first image processing apparatus 20, and the display device 40displays the image that corresponds to the image data image-processed bythe second image processing apparatus 30.

Causes for Occurrence of RTS Noise and Characteristics of RTS Noise

Next, causes for occurrence of the RTS noise and characteristics of theRTS noise will be described.

FIG. 3 is a diagram illustrating variation of amplifier output from theamplifier unit 105 e at the time of occurrence of RTS noise in a casewhere light is shielded so as to avoid the light from reaching the imagesensor 105. FIG. 4 is a diagram illustrating distribution of pixelvalues read out using the amplifier unit 105 e on which RTS noiseoccurs.

In a case where a trap level is present on a gate oxide film of theamplifier unit 105 e, RTS noise occurs when electrical charges aretrapped or discharged at the trap level at random timings. Therefore, asillustrated in FIG. 3, amplifier output randomly varies in a range ofapproximately Vrts on the amplifier unit 105 e on which RTS noiseoccurs. Moreover, variation in potential does not occur in an instantbut requires a slight length of time r.

In a typical case of the image sensor 105, correlated double samplingprocessing (hereinafter, referred to as CDS processing) is performed inorder to reduce noise from the pixel value read out from the pixel 105a. In the CDS processing, the imaging controller 114 turns on a resetswitch (not illustrated) of the image sensor 105 so as to reset theelectrical charges of the FD unit 105 d, and furthermore, the imagingcontroller 114 turns on the second switch 105 f to produce a resetstate, and reads out (outputs) a signal (reference signal) in the resetstate. Next, in CDS processing, the imaging controller 114 turns on thefirst switch 105 b (or any of first switches 105 b(1) to 105 b(8)) aloneso as to transfer the electrical charges generated on the pixel 105 a tothe FD unit 105 d, and furthermore, turns on the second switch 105 f tocome into a read-out state (output state) and reads out (outputs) asignal in the read-out state. Subsequently, in the CDS processing, thesignal obtained by subtracting a reset state signal (reference signal)from the read-out signal is converted as a pixel value.

As illustrated in FIG. 3, when the image sensor 105 reads out individualsignals of a timing tr1 (reset state) and a timing ts1 (read-out state)by the CDS processing, individual amplifier output values V for thetiming tr1 and the timing ts1 are substantially similar to each other.Accordingly, the image sensor 105 receives mainly an effect of randomnoise, resulting in the pixel values that have been read out taking aform of distribution A around zero, as illustrated in FIG. 4. Similarly,even in a case of a timing tr2 (reset state) and a timing ts2 (read-outstate) on the image sensor 105, individual amplifier output values V forthe timing tr2 and the timing ts2 are substantially similar to eachother. As a result, the pixel values that have been read out take a formof the distribution A illustrated in FIG. 4.

The image sensor 105 reads out individual signals at a timing tr3 (resetstate) and a timing ts3 (read-out state) by CDS processing. At thistime, the amplifier output of the timing ts3 is lower than the amplifieroutput of the timing tr3 by approximately Vrts. Accordingly, due to adifference between the two signals, a shift occurs in the negativedirection by RTS_Value, that is, the pixel value that corresponds toVrts, which is the change amount of amplifier output, resulting indistribution B in which read out pixel values are distributed around−RTS_Value.

In contrast, the image sensor 105 reads out individual signals at atiming tr4 (reset state) and a timing ts4 (read-out state) by CDSprocessing. At this time, the amplifier output of the timing ts4 ishigher than the amplifier output of the timing tr4 by approximatelyVrts. Accordingly, due to a difference between the two signals, a shiftoccurs in the positive direction by RTS_Value, that is, the pixel valuethat corresponds to Vrts, which is the change amount of amplifieroutput, resulting in distribution C in which read out pixel values aredistributed around RTS_Value.

Herein, since the occurrence of variation of amplifier output in FIG. 3needs the length of time t, there may be a case where a signal is readout during variation of the potential. In this case, an amplifier outputdifference is greater than −Vrts and smaller than Vrts, between resetstate read out timing and read-out-state read-out timing. As a result,the pixel value read out from the image sensor 105 is also greater than−RTS_Value and smaller than RTS_Value. Since the length of time τ isconsidered to be substantially constant provided that the conditions(for example, temperature and drive voltage) of the image sensor 105 isconstant, the pixel value greater than −RTS_Value and smaller thanRTS_Value occurs with a similar probability. Herein, the frequency ofoccurrence of these pixel values is defines as a noise. Moreover, whilehaving different median values, distribution B and distribution C aresimilar distributions other than the median value. Accordingly,hereinafter, the ratio of distribution B or distribution C todistribution A will be defined as arts. The shorter the amplifier outputvariation cycle of the amplifier unit 105 e, the greater arts.

In this manner, the pixel value read out by CDS processing using theamplifier unit 105 e involving occurrence of RTS noise results in thedistribution illustrated in FIG. 4. Under the condition where light isapplied to the image sensor 105, the potential in the read-out statechanges in accordance with the exposure amount. The change in thepotential due to RTS noise, however, is constant independently from theexposure amount. That is, the RTS noise has a characteristic of randomlyvarying with respect to a normal pixel value within a range not lessthan −RTS_Value and not greater than RTS_Value, independently from theexposure amount. While FIG. 4 schematically illustrates distribution A,distribution B, and distribution C, distribution typically takes a formof normal distribution.

Moreover, since the RTS noise is noise attributed to the read-outcircuit (amplifier unit 105 e), in a case where each of the plurality ofpixels 105 a shares one read-out circuit as illustrated in FIG. 2, RTSnoise with a similar characteristic occurs on all sharing pixels (pixel105 a(1) to 105 a(8)).

Moreover, the RTS noise might occur also in cases other than theread-out circuit (amplifier unit 105 e), illustrated in FIG. 2, that is,in a column amplifier and a source follower, or the like, shared in acolumn direction of the image sensor 105. In this case, RTS noise with asimilar characteristic occurs also on all pixels, in the columndirection, sharing a same column amplifier and a source follower. In thepresent embodiment, application is possible to the RTS noise occurred inthe circuit other than the read-out circuit (amplifier unit 105 e).

In this manner, the RTS noise is a type of blinking defect noise thatinvolves amplitude (variation) of the pixel value of the image obtainedby shooting, within a fixed range (not less than −RTS_Value and notgreater than RTS_Value) in a case where a fixed subject is shot under asame condition.

Processing on First Image Processing Apparatus

Next, processing of the first image processing apparatus 20 will bedescribed FIG. 5 is a flowchart illustrating an outline of processingexecuted by the first image processing apparatus 20, that is, aflowchart of a main routine executed by the first image processingapparatus 20. With FIG. 5, RTS noise detection processing of detectingRTS noise, executed by the first image processing apparatus 20 will bedescribed.

As illustrated in FIG. 5, the RTS noise detection unit 202 outputs aninstruction signal to shoot a light-shielded image, to the imagingcontroller 114 via the second external I/F unit 201 and the firstexternal I/F unit 115, and causes the imaging controller 114 to shoot aplurality of (for example, 250) dark-time RAW images (step S101). Inthis case, the imaging controller 114 causes the image sensor 105 toshoot the dark-time RAW image by controlling the shutter 103 and theimage sensor 105 based on the instruction signal input from the RTSnoise detection unit 202. At this time, in a case where the RAW imagesize and the drive mode (for example, high-speed read-out mode or imagequality preference mode) of the image sensor 105 at shooting aredifferent, and in a case where the characteristics of the RAW image (forexample, bit number) are different, in each of a still image, a movingimage, or a live-view image, it is preferable that the image sensor 105shoots a plurality of dark-time RAW images for each of individual cases.Moreover, the imaging controller 114 outputs the plurality of dark-timeRAW images captured by the image sensor 105, to the RTS noise detectionunit 202, via the bus 113, the first external I/F unit 115, and thesecond external I/F unit 201.

The RAW image data to be output to the RTS noise detection unit 202 neednot be a dark-time RAW image, that is, it is possible to detect RTSnoise with a plurality of pieces of RAW image data obtained by shootingthe same subject. It would be preferable, however, to use a dark-timeRAW image because the degree of effects of random noise decreases indark in typical cases. For similar reasons, as the RAW image data to beoutput to the RTS noise detection unit 202, it may be more preferable toshoot, with the image sensor 105, a plurality of dark-time RAW images inlow temperature (room temperature or below) rather than hightemperature, because random noise is less in the low temperature.Moreover, RAW image data to be output to the RTS noise detection unit202 need not be a plurality of pieces of RAW image data constituted withthe signal output from each of the plurality of pixels 105 a, but may bea plurality of pieces of RAW image data constituted with a signal outputsolely from the FD unit 105 d.

Subsequently, the RTS noise detection unit 202 executes isolated pointdetection and pixel value group creation processing (step S102) thatincludes detection of an isolated point (defective pixel) and creationof a plurality of pixel value groups using a plurality of dark-time RAWimages obtained from the imaging apparatus 10.

FIG. 6 is a flowchart illustrating an outline of isolated pointdetection and pixel value group creation processing in step S102 in FIG.5.

As illustrated in FIG. 6, the isolated point detection unit 202 a firstgenerates (step S201) an average dark-time RAW image, that is, anaverage of the plurality of dark-time RAW images obtained inabove-described step S101. Specifically, the isolated point detectionunit 202 a generates the average dark-time RAW image by first addingpixel values of all dark-time RAW images for each of the pixels, andthen dividing the sum by the number (for example, 250) of dark-time RAWimages. With this processing, it is possible to reduce the effects ofrandom noise and RTS noise in the average dark-time RAW image. Theisolated point detection unit 202 a may generate the average dark-timeRAW image using solely a portion of the dark-time RAW image shot in stepS101 described above. This could reduce the time to create the averagedark-time RAW image.

Moreover, under the condition where the image sensor 105 has stablecharacteristics, the isolated point detection unit 202 a may generatethe average dark-time RAW image using another dark-time RAW image shotin a timing that differs from the timing of the case of shooting in stepS101 described above. Accordingly, with above-described steps S101 andS201 performed in parallel, or with above-described step S101 performedin parallel with steps S203 and S204 described below, the isolated pointdetection unit 202 a need not store all the dark-time RAW images shot inabove-described step S101, in the non-volatile memory 112, making itpossible to create a pixel value group even with the volatile memory 111with low capacity.

Subsequently, the isolated point detection unit 202 a determines anisolated point pixel based on the average dark-time RAW image (stepS202). Specifically, the isolated point detection unit 202 a determineswhether the pixel value of each of the pixels constituting an averagedark-time RAW image is a predetermined threshold (first threshold) orabove, and then, determines (detects) this pixel having the pixel valueof the determined threshold (first threshold) or above as an isolatedpoint (defective pixel). The predetermined threshold is, for example,approximately double the pixel value of dark-time shot noise. Thisenables accurate detection (determination) of the isolated point pixel(defective pixel) having a possibility of lowering RTS noise detectionaccuracy. While the isolated point detection unit 202 a detects thepixel that outputs the pixel value greater than the predeterminedthreshold (first threshold) as a white spot pixel, as an isolated pointpixel, the isolated point detection unit 202 a may detect a black spotpixel, for example, having a pixel value smaller than a predeterminedthreshold (second threshold). The black spot pixel is attributed to poorsensitivity, that is, a case where the sensitivity is lower than thesensitivity of surrounding pixels, defective photodiode (pixel 105 a),or the like. In a case where the cause is poor sensitivity, this pixelwould not affect the RTS noise detection due to the use of a dark-timeRAW image, even without being detected as an isolated point pixel.However, in a case where the cause is in the pixel 105 a, it would bepreferable that the pixel is detected similarly to the above-describedwhite spot. Accordingly, in addition to the detection of the white spotpixel, the isolated point detection unit 202 a may further detect apixel having a pixel value not greater than the predetermined value(second threshold) as a black spot pixel based on a plurality of imagesobtained by shooting a uniform subject. This processing can enhance RTSnoise detection accuracy.

Thereafter, the pixel value group creation unit 202 b subtracts theaverage dark-time RAW image from each of the plurality of dark-time RAWimages (step S203). Specifically, the pixel value group creation unit202 b subtracts a pixel value of a pixel on an average dark-time RAWimage from the pixel value of each of the pixels constituting each ofthe dark-time RAW images. In this case, the pixel value group creationunit 202 b also retains a negative value in a result of subtraction.Moreover, the image sensor 105 typically includes an OB area (OB pixel)for performing offset detection (optical black detection) for electricalcharges including dark current. Accordingly, the pixel value groupcreation unit 202 b may subtract the average of the pixel values in theOB area from each of the plurality of dark-time RAW images. Using theaverage dark-time RAW image can exclude effects of shading, or the likedue to variation of dark current of each of the pixels, and thus candetect RTS noise further accurately.

Subsequently, based on an isolated point pixel determination resultobtained by detection by the isolated point detection unit 202 a in stepS202 and based on the plurality of dark-time RAW images from which theaverage dark-time RAW image has been subtracted in step S203, the pixelvalue group creation unit 202 b classifies the pixel values for each ofthe read-out circuits (amplifier units 105 e) and creates a plurality ofpixel value groups (step S204). Specifically, the pixel value groupcreation unit 202 b first excludes a pixel value output from the pixeldetermined as the isolated point pixel from the pixel values of all thepixels of the plurality of dark-time RAW images from which the averagedark-time RAW image has been subtracted. Next, the pixel value groupcreation unit 202 b creates groups for each of the pixel values outputfrom the pixel read out using the same read-out circuit (amplifier unit105 e) and defines these groups as individual pixel value groups.

FIG. 7 is a diagram schematically illustrating a pixel of the imagesensor 105 that outputs a pixel value classified into a same pixel valuegroup. FIG. 7 illustrates a case of sharing pixels in which a signal isread out by one read-out circuit (amplifier unit 105 e) for eightpixels, that is, two pixels in the lateral direction×four pixels in thelongitudinal direction, illustrated in FIG. 2. Moreover, in FIG. 7, asquare P1 formed with a thin line L1 indicates each of pixels,specifically, the white square P1 indicates a normal pixel while a blacksquare P2 indicates an isolated point pixel Furthermore, in FIG. 7, aframe formed with a thick line L2 represents a pixel that outputs apixel value classified into a same pixel value group. Moreover, FIG. 7illustrates, in an order from the top, a portion of an image shot in aW1-th time, a portion of an image shot in a W2-th time, and a portion ofan image shot in a W3-th time.

As illustrated in FIG. 7, a pixel value group A is a set of pixel valuesoutput from each of the pixels in the thick frame portion L2 on theupper left, on each of the plurality of the dark-time RAW images fromwhich the pixel value group creation unit 202 b has subtracted theaverage dark-time RAW image. In this case, when 250 dark-time RAW imagesare used, for example, the pixel value group A would be a set of8×250=2000 pixel values. Moreover, a pixel value group X is a set ofpixel values output from the pixels with no detected isolated point(defective pixel), among the pixels within the thick frame portion. Inthis case, when 250 dark-time RAW images are used, for example, thepixel value group X would be a set of (8−1)×250=1750 pixel values.

After step S204, the RTS noise detection unit 202 returns to the mainroutine in FIG. 5.

Returning to FIG. 5, description of step S103 and subsequent stepsfollows below.

In step S103, the RTS noise detection unit 202 performs average randomnoise amount calculation and RTS flag setting processing on a pluralityof pixel value groups. The average random noise amount calculation andRTS flag setting processing includes setting of an RTS flag indicatingthe possibility of occurrence of the RTS noise, and calculation of theaverage random noise amount of a pixel value group.

FIG. 8 is a flowchart illustrating an outline of average random noiseamount calculation and RTS flag setting processing illustrated in stepS103 in FIG. 5.

As illustrated in FIG. 8, the average random noise amount calculationunit 202 c first calculates the random noise amount of each of the pixelvalue groups (step S301). Specifically, the average random noise amountcalculation unit 202 c calculates a single value for the random noiseamount for a single pixel value group. In the first embodiment, theaverage random noise amount calculation unit 202 c calculates standarddeviation of the pixel values constituting a pixel value group, andcalculates the standard deviation as the random noise amount of each ofthe pixel value groups. For example, the average random noise amountcalculation unit 202 c calculates standard deviation of 2000 pixelvalues in the case of the pixel value group A, and calculates standarddeviation of 1750 pixel values in the case of the pixel value group X,as illustrated in FIG. 7. Alternatively, the average random noise amountcalculation unit 202 c may calculate a value as the random noise amountusing other scales such as a maximum value, a minimum value, adifference between the maximum and minimum values, or a dispersionvalue.

Subsequently, the average random noise amount calculation unit 202 csets the RTS flag to a clear state for each of pixel value group (stepS302). The RTS flag is information that accompanies each of the pixelvalue group. The clear state of the RTS flag indicates no possibility ofoccurrence of RTS noise in the corresponding pixel value group, whilethe set state of the RTS flag indicates possibility of occurrence of theRTS noise in the corresponding pixel value group.

Thereafter, the average random noise amount calculation unit 202 ccalculates an average of the average random noise amounts of all thepixel value groups for which the RTS flag is set to the clear state(step S303). Specifically, the average random noise amount calculationunit 202 c calculates the average of the standard deviation of the pixelvalue group for which the RTS flag calculated in step S301 is set to theclear state, as the random noise amount of the plurality of dark-timeRAW images, and defines the calculation result as the average randomnoise amount.

Subsequently, the average random noise amount calculation unit 202 ccompares the random noise amount with the average random noise amount,in the pixel value group, for each of the pixel value group for whichthe RTS flag is set to the clear state. Then, the average random noiseamount calculation unit 202 c executes RTS flag setting processing,specifically, sets the RTS flag to the set state when the random noiseamount is greater (step S304).

FIG. 9 is a flowchart illustrating an outline of processing in the RTSflag setting processing in step S304 in FIG. 8.

As illustrated in FIG. 9, the average random noise amount calculationunit 202 c first sets (step S401) a pixel-of-interest value group neededfor sequentially processing of steps S402 to S404 described below (thatis, sets a pointer for specifying a group that executes processing ofsteps S402 to S404, described below). Specifically, the average randomnoise amount calculation unit 202 c allocates integers greater thanzero, in the raster order, such as 1, 2, 3 . . . from upper left tolower right, as an index, for each of sharing pixel blocks (for each ofread-out circuits) on the image sensor 105. Next, the average randomnoise amount calculation unit 202 c increments a counter by one everytime step S401 is executed (the counter is reset to zero at the pointwhen the RTS flag setting processing is started). The average randomnoise amount calculation unit 202 c sets a pixel value group to whichthe index indicated by the counter is allocated, as thepixel-of-interest value group. That is, when step S401 is first executedby the average random noise amount calculation unit 202 c, the averagerandom noise amount calculation unit 202 c increments the counter, whichhas been reset to zero, by one. Accordingly, the counter indicates oneand the pixel value group in the upper left portion becomes thepixel-of-interest value group. When the average random noise amountcalculation unit 202 c executes processing of step S401 twice (secondtime), the counter indicates two. Accordingly, the pixel value group onthe right side of the upper left pixel value group becomes thepixel-of-interest value group.

Subsequently, the average random noise amount calculation unit 202 cdetermines whether the RTS flag of the pixel of interest group is in theset state (step S402). In a case where the average random noise amountcalculation unit 202 c determines that the RTS flag of the pixel ofinterest group is in the set state (step S402: Yes), the first imageprocessing apparatus 20 proceeds to step S405 described below. Incontrast, in a case where the average random noise amount calculationunit 202 c determines that the RTS flag of the pixel of interest groupis not in the set state (step S402: No), the first image processingapparatus 20 proceeds to step S403 described below.

In step S403, the average random noise amount calculation unit 202 cdetermines whether the random noise amount of the pixel-of-interestvalue group is greater the average random noise amount. Specifically,the average random noise amount calculation unit 202 c determineswhether the standard deviation of the pixel-of-interest value group isgreater than a value obtained by multiplying the average of the standarddeviation of the average random noise amount by a predeterminedcoefficient (for example, 1.5). In a case where the average random noiseamount calculation unit 202 c determines that the random noise amount ofthe pixel-of-interest value group is greater the average random noiseamount (step S403: Yes), the first image processing apparatus 20proceeds to step S404 described below. In contrast, in a case where theaverage random noise amount calculation unit 202 c determines that therandom noise amount of the pixel-of-interest value group is not greaterthe average random noise amount (step S403: No), the first imageprocessing apparatus 20 proceeds to step S405 described below.

In step S404, the average random noise amount calculation unit 202 csets the RTS flag of the pixel-of-interest value group to the set state.

Subsequently, the average random noise amount calculation unit 202 cdetermines, for all the pixel value groups, whether the processing inabove-described steps S401 to S404 is finished (step S405). In a casewhere the average random noise amount calculation unit 202 c determines,for all the pixel value groups, that processing of above-described stepsS401 to S404 is finished (step S405: Yes), the first image processingapparatus 20 returns to the average random noise amount calculation andRTS flag setting processing in FIG. 8. In contrast, in a case where theaverage random noise amount calculation unit 202 c determines that theprocessing in above-described steps S401 to S404 is not finished (stepS405: No), the first image processing apparatus 20 returns toabove-described step S401.

Returning to FIG. 8, description of step S305 and subsequent stepsfollows below.

In step S305, the average random noise amount calculation unit 202 cdetermines whether the RTS flag has been newly set to the set state inthe RTS flag setting processing in above-described step S304.Specifically, the average random noise amount calculation unit 202 cdetermines whether processing of step S404 in FIG. 9 has been executedone or more times in the processing of step S304 previously executed. Ina case where the average random noise amount calculation unit 202 cdetermines that the RTS flag has been newly set to the set state(processing of step S404 in FIG. 9 has been executed one or more timesin the processing of step S304 previously executed) (step S305: Yes),the first image processing apparatus 20 returns to step S303. Incontrast, in a case where the average random noise amount calculationunit 202 c determines that the RTS flag has not been newly set to theset state (processing of step S404 in FIG. 9 has not been executed inthe processing of step S304 previously executed) (step S305: No), thefirst image processing apparatus 20 returns to the main routine in FIG.5. While the average random noise amount calculation unit 202 c performsdetermination, in step S305, by the number of times of execution of stepS404 in above-described FIG. 9, it is also possible to determine whetherthe number of times of execution of above-described steps S403 to S405has reached a predetermined number of times, for example, three times.

In this manner, when the average random noise amount calculation unit202 c executes processing illustrated in above-described FIGS. 8 and 9,RTS flags of all the pixel value groups are set to the clear state inabove-described step S302, and it is possible to calculate the averagerandom noise amount of all the pixel value groups by executing initiallydescribed step S303. The random noise amount is great in the pixel valuegroup in which the RTS noise occurs. Accordingly, the average randomnoise amount calculation unit 202 c performs comparison with the averagerandom noise amount in above-described step S304, thereby setting theRTS flag to the set state for the pixel value group in which RTS noiseoccurs.

The random noise amount of the pixel value group having small RTS_Value,however, has a smaller difference when compared with the random noiseamount of the pixel value group in which the RTS noise does not occur.Therefore, in some cases, the RTS flag cannot be properly set (properlyset to the set state) on the pixel value group having small RTS_Valuemerely by the execution of above-described step S303 and step S304 onceby the average random noise amount calculation unit 202 c. To cope withthis, the first embodiment causes the average random noise amountcalculation unit 202 c to repeat above-described steps S303 to S305,thereby enabling setting of the RTS flag on the pixel value group havingsmall RTS_Value. Furthermore, it is possible to gradually converge theaverage random noise amount. As a result, this enables the averagerandom noise amount calculation unit 202 c to set the RTS flag to theset state for all the pixel value groups having RTS noise of a leveldiscernable from the random noise. In contrast, the RTS flag remains tobe set to the clear state in all the pixel value groups having RTS noiseof a level indiscernible from the random noise. Nevertheless, since thistype of RTS noise is buried among the random noise, it need not bedetected or corrected.

Moreover, as illustrated in FIG. 3 described above, since distributionis produced on the pixel values shifted by RTS_Value due to RTS noise inthe pixel value group in which RTS noise occurs, the standard deviationis increased. Since there is a low possibility of occurrence of the RTSnoise in all of the read-out circuits (amplifier unit 105 e), it ispossible to reliably classify the pixel value groups into the pixelvalue group having possibility of occurrence of RTS noise that needsdetection and correction, and the pixel value group having nopossibility of occurrence of RTS noise that needs detection andcorrection, by execution of the processing illustrated inabove-described FIGS. 8 and 9 by the average random noise amountcalculation unit 202 c.

Returning to FIG. 5, description of step S104 and subsequent stepsfollows below.

In step S104, the RTS noise detection unit 202 executes RTS noisefeature data calculation processing (step S104) of calculating the RTSfeature data representing feature of the RTS noise, onto the pixel valuegroup in which the RTS flag has been set to the set state in step S103.

FIG. 10 is a flowchart illustrating an outline of RTS noise feature datacalculation processing in step S104 in FIG. 5.

As illustrated in FIG. 10, the RTS noise feature data calculation unit202 d first creates a histogram (step S501) in which each of the pixelvalues within a pixel value group has been converted into an absolutevalue, for each of the pixel value group in which the RTS flag has beenset to the set state. Specifically, in a case where the distribution ofthe pixel value group takes a form illustrated in above-described FIG.4, the RTS noise feature data calculation unit 202 d performs conversioninto absolute values and creates a histogram illustrated in FIG. 11.Distribution B+C is distribution obtained by combining distribution Bwith distribution C in FIG. 4, and thus, the total number ofdistribution A×αRTS×2 is equal to the total number of distribution B+C.Moreover, the distribution of the number below the RTS_Value due to RTSnoise becomes 2×αnoise. The RTS noise feature data calculation unit 202d can bias the distribution by executing the processing of step S501,making it possible to obtain accurate distribution without increasingthe number of dark-time RAW images.

Subsequently, the RTS noise feature data calculation unit 202 d performsfitting on each of the pixel value groups in which the RTS flag has beenset to the set state, with the RTS noise model, thereby calculating theRTS_Value (step S502). Specifically, when it is assumed that the randomnoise occurs with normal distribution of standard deviation σ,distribution f(x) in FIG. 4 can be expressed in Formulas (1) and (2)using a coefficient k to match the total number of the pixel valuegroups and accumulation of f(x).

$\begin{matrix}{{{f(x)} = {k\lbrack {{\frac{1}{\sqrt{2{\pi\sigma}^{2}}}{\exp( {- \frac{x^{2}}{2\sigma^{2}}} )}} + {\alpha\;{RTS}\{ {\frac{\exp\lbrack {- \frac{( {x - {RTS\_ Value}} )^{2}}{2\sigma^{2}}} \rbrack}{\sqrt{2{\pi\sigma}^{2}}} + \frac{\exp\lbrack {- \frac{( {x + {RTS\_ Value}} )^{2}}{2\sigma^{2}}} \rbrack}{\sqrt{2{\pi\sigma}^{2}}}} \}} + {\alpha noise}} \rbrack}}( {{- {RTS\_ Value}} \leqq x \leqq {RTS\_ Value}} )} & (1) \\{{{f(x)} = {k\lbrack {{\frac{1}{\sqrt{2{\pi\sigma}^{2}}}{\exp( {- \frac{x^{2}}{2\sigma^{2}}} )}} + {\alpha\;{RTS}\{ {\frac{\exp\lbrack {- \frac{( {x - {RTS\_ Value}} )^{2}}{2\sigma^{2}}} \rbrack}{\sqrt{2{\pi\sigma}^{2}}} + \frac{\exp\lbrack {- \frac{( {x + {RTS\_ Value}} )^{2}}{2\sigma^{2}}} \rbrack}{\sqrt{2{\pi\sigma}^{2}}}} \}}} \rbrack}}\;( {{Outside}\mspace{14mu}{the}\mspace{14mu}{above}\text{-}{described}\mspace{14mu}{range}} )} & (2)\end{matrix}$

While the range of occurrence of αnoise is defined as the range greaterthan −RTS_Value and smaller than RTS_Value, the value range of xdescribed in Formula (1) includes equal signs. Here, the value rangeincluding equal signs is used in view of rounding in sampling in A/Dconversion by the A/D converter 107. Accordingly, distribution g(x) inFIG. 11 results in Formula (3).

$\begin{matrix}{{g(x)} = \{ \begin{matrix}{f(0)} & {{{for}\mspace{14mu} x} = 0} \\{2\;{f(x)}} & {{{for}\mspace{14mu} x} \neq 0}\end{matrix} } & (3)\end{matrix}$

In this manner, using the standard deviation a, RTS_Value, αRTS, andαnoise as variables, the RTS noise feature data calculation unit 202 dcalculates g(x) with x being within a predetermined range (for example,10-bit range of approximately twice the maximum value available within 0to RTS_Value), and performs normalization, and then calculates thestandard deviation a, RTS_Value, αRTS, and αnoise, capable of obtaininga minimum error between the absolute value histogram and g(x).Specifically, using the standard deviation a, RTS_Value, αRTS, andαnoise as variables, the RTS noise feature data calculation unit 202 drefers to g(x) with x being within a predetermined range, sets k suchthat the total number of g(x) calculated within the above-describedpredetermined range matches with the total number of the pixel valuesincluded in the pixel value group, and then, calculates the standarddeviation a, RTS_Value, αRTS, and αnoise, capable of obtaining a minimumerror between the absolute value histogram and g(x) (RTS_ValueCalculation Method 1). As an alternative method for obtaining the error,the RTS noise feature data calculation unit 202 d may obtain the valueaccumulated with weighting in accordance with the pixel value x, otherthan the value being simple accumulation of errors of individual pixelvalues x. Since the random noise occurs within the image sensor 105 insubstantially a same manner, the standard deviation a becomes a valuearound the average standard deviation calculated as the average randomnoise amount. Accordingly, it may be possible to use the average randomnoise amount, that is, it may be possible to use the standard deviationcalculated in the end of step S303, as the standard deviation a. Thismakes it possible to reduce the calculation amount.

After step S502, the RTS noise feature data calculation unit 202 dcalculates a deviation amount for each of the pixel value groups inwhich the RTS flag is set to the set state (step S503). Herein, a medianvalue in a range that distributes with a predetermined ratio (e.g.approximately 0.1% to 0.2%) of the quantity included in the pixel valuegroups, from the greater pixel value, on the absolute value histogram,is defined as the deviation amount (when the predetermined ratio isassumed to be the number of pixel values included in the 1/pixel valuegroup, the deviation amount matches with the maximum value of the pixelvalue group). In a case where αRTS is extremely small, there is apossibility that the RTS noise feature data calculation unit 202 d hasdifficulty in calculating the RTS noise feature data merely with RTSnoise model fitting in above-described step S502. Accordingly, it wouldbe possible to avoid miscalculation of the RTS noise feature data byusing the deviation amount. Alternatively, the average random noiseamount calculation unit 202 c may calculate the deviation amount foreach of the pixel value groups in which the RTS flag is set to the setstate, and may perform RTS flag setting using this deviation amount asthe random noise amount of each of the pixel value groups. This canprevent false negative in detection of the RTS noise with extremelysmall αRTS. After step S503, the first image processing apparatus 20returns to the main routine in FIG. 5.

Returning to FIG. 5, description of step S105 and subsequent stepsfollows below.

In step S105, the RTS noise detection unit 202 executes RTSdetermination processing. The RTS determination processing includesdetermination of whether RTS noise of the level requiring correction isoccurring in each of the pixel value groups based on the RTS featuredata of the pixel value group in which the RTS flag is set to the setstate, and includes recording of RTS noise information into thenon-volatile memory 112 of the imaging apparatus 10. The RTS noiseinformation is information that associates a result of the determinationwith a position of the read-out circuit that corresponds to each of thepixel value groups (that is, position (address) of a sharing pixel blockincluding the pixels 105 a that share the read-out circuit). After stepS105, the first image processing apparatus 20 finishes the currentprocessing.

FIG. 12 is a flowchart illustrating an outline of RTS noisedetermination processing of step S105 in FIG. 5.

As illustrated in FIG. 12, the RTS noise determination unit 202 e firstclears the RTS noise information recorded in the non-volatile memory 112via the second external I/F unit 201, the first external I/F unit 115,and the bus 113 (step S601).

Subsequently, the RTS noise determination unit 202 e sets apixel-of-interest value group to be used for sequentially performingprocessing in steps S603 to S605 described below (step S602).Specifically, the RTS noise determination unit 202 e sets a pointer forspecifying a group that undergoes processing in steps S603 to S605described below. For example, the RTS noise determination unit 202 eallocates beforehand integers greater than zero, in the raster order,such as 1, 2, 3 . . . from upper left to lower right, as an index, foreach of sharing pixel blocks on the image sensor 105. Next, the RTSnoise determination unit 202 e increments a counter by one every timestep S602 is executed (counter is reset to zero at the point when theRTS noise determination processing in FIG. 12 is started). The pixelvalue group to which the index indicated by the counter is allocated isdefined as the pixel-of-interest value group. That is, when step S602 isfirst executed by the RTS noise determination unit 202 e, the RTS noisedetermination unit 202 e increments the counter, which has been reset tozero, by one. Accordingly, the counter indicates one and the pixel valuegroup in the upper left portion becomes the pixel-of-interest valuegroup. When the RTS noise determination unit 202 e executes processingof step S602 twice (second time), the counter indicates two.Accordingly, the pixel value group on the right side of the upper-leftpixel value group becomes the pixel-of-interest value group.

Thereafter, the RTS noise determination unit 202 e determines whetherthe RTS flag is set to the set state, for the pixel value group set asthe pixel-of-interest value group (step S603). In a case where the RTSnoise determination unit 202 e determines that the RTS flag is set tothe set state for the pixel value group set as the pixel-of-interestvalue group (step S603: Yes), the first image processing apparatus 20proceeds to step S604 described below. In contrast, in a case where theRTS noise determination unit 202 e determines that the RTS flag is notset to the set state for the pixel value group set as thepixel-of-interest value group (step S603: No), the first imageprocessing apparatus 20 proceeds to step S608 described below.

In step S604, the RTS noise determination unit 202 e determines whetherthe RTS_Value calculated by the RTS noise feature data calculation unit202 d in above-described step S502 in FIG. 10 is a predetermined valueor above, that is, ThValue or above, for example. ThValue is a valueobtained by multiplying the average random noise amount by apredetermined coefficient (for example, 2.0) since small RTS noise isunnoticeable by a human due to random noise. In step S604, the RTS noisedetermination unit 202 e performs comparison between RTS_Value andThValue. In a case where the RTS_Value is significantly different fromthe deviation amount, however, fitting by the RTS noise feature datacalculation unit 202 d might have failed in above-described step S502 inFIG. 10. Therefore, the RTS noise determination unit 202 e may furtherperform determination whether the difference between the RTS_Value andthe deviation amount is small. In a case where the RTS noisedetermination unit 202 e determines that the RTS_Value is ThValue orabove (step S604: Yes), the first image processing apparatus 20 proceedsto step S605 described below. In contrast, in a case where the RTS noisedetermination unit 202 e determines that the RTS_Value is not ThValue orabove (step S604: No), the first image processing apparatus 20 proceedsto step S606 described below.

In step S606, the RTS noise determination unit 202 e determines whetherthe deviation amount of the pixel of interest group calculated by theRTS noise feature data calculation unit 202 d in above-described stepS503 is a predetermined value or above, for example, ThMax or above.Specifically, the RTS noise determination unit 202 e performsdetermination by using the deviation amount in order to detect(determine) the RTS noise with a low frequency of occurrence. This isbecause in a case where the frequency of occurrence is low, the RTSnoise is more unnoticeable by a human than the random noise. ThMax ispreferably the value of ThValue or above. In a case where the RTS noisedetermination unit 202 e determines that the deviation amount of thepixel of interest group is ThMax or above (step S606: Yes), the firstimage processing apparatus 20 proceeds to step S607 described below. Incontrast, in a case where the RTS noise determination unit 202 edetermines that the deviation amount of the pixel of interest group isnot ThMax or above (step S606: No), the first image processing apparatus20 proceeds to step S608 described below.

In step S607, the RTS noise determination unit 202 e sets the deviationamount of the pixel of interest calculated by the RTS noise feature datacalculation unit 202 d in above-described step S503, as the RTS_Value ofthe pixel-of-interest value group (value of deviation amount isduplicated as a value of RTS_Value). After step S607, the first imageprocessing apparatus 20 proceeds to step S608.

Subsequently, the RTS noise determination unit 202 e determines theRTS_Value as an RTS noise level, and associates the RTS_Value withpositional information (address information) of the read-out circuit(amplifier unit 105 e) that outputs a pixel value constituting the pixelvalue group set as the pixel-of-interest value group, that is,positional information (address information) of a sharing pixel blockincluding solely pixels sharing the read-out circuit, and temporarilystores the associated information and the value in the volatile memory111 of the imaging apparatus 10 (step S605).

Thereafter, the RTS noise determination unit 202 e determines whetherthe setting of the pixel-of-interest value group and the processing inabove-described steps S603 to S605 have been performed on all the pixelvalue groups (step S608). In a case where the RTS noise determinationunit 202 e determines that the setting and the processing have beenperformed on all the pixel value groups (step S608: Yes), the firstimage processing apparatus 20 proceeds to step S609 described below. Incontrast, in a case where the RTS noise determination unit 202 edetermines that the setting and the processing have not been performedon all the pixel value groups (step S608: No), the first imageprocessing apparatus 20 returns to step S602.

In step S609, the RTS noise determination unit 202 e calculates amaximum value of the RTS_Value temporarily stored in the volatile memory111 of the imaging apparatus 10 via the second external I/F unit 201,the first external I/F unit 115, and the bus 113, and calculates thehistogram of the RTS_Value. Then, the RTS noise determination unit 202 eassociates the correspondence between the positional information of thesharing pixel block temporarily stored in the volatile memory 111 andthe RTS_Value, the maximum value of the RTS_Value, and the histogram ofthe RTS_Value, with each other, and records them, as RTS noiseinformation, into the RTS noise information recording unit 112 b of thenon-volatile memory 112. The correspondence between the positionalinformation of the sharing pixel block and the RTS_Value means thepositional information of the sharing blocks having possibility ofoccurrence of RTS noise and the RTS_Value for each of the sharingblocks. After step S609, the first image processing apparatus 20 returnsto the main routine in FIG. 5 and the current processing is finished.The RTS noise determination unit 202 e has associated the positionalinformation of a sharing pixel block (positional information of read-outcircuit (amplifier unit 105 e)) and the RTS noise feature data with eachother and recorded them as the RTS noise information, in the RTS noiseinformation recording unit 112 b of the non-volatile memory 112. Sinceeach of the pixels belongs to any of the sharing pixel blocks, it may bepossible to record as RTS noise information that associates positionalinformation (pixel coordinate information) on each of the plurality ofpixels and the RTS noise feature data with each other, in thenon-volatile memory 112. In this case, since the number of pixels is thenumber of sharing pixel blocks or more, the amount of data to berecorded on the non-volatile memory 112 would be increased. However,there would be no need to consider the correspondence between each ofthe pixels and the sharing pixel block at correction of the RTS noise.Additionally, the RTS noise feature data include the correspondence ofthe RTS_Value, the maximum value of the RTS_Value, and the histogram ofthe RTS_Value.

Moreover, in above-described FIG. 12, while the RTS noise determinationunit 202 e first clears the RTS noise information recorded on the RTSnoise information recording unit 112 b of the non-volatile memory 112 ofthe imaging apparatus 10, and thereafter records the RTS noiseinformation detected in above-described steps S602 to S607 into thenon-volatile memory 112, the RTS noise is not always produced in thestage of production of the image sensor 105, but might be produced byaging and be increased gradually. Therefore, the RTS noise determinationunit 202 e may update the RTS noise information by adding newly detectedRTS noise information, without clearing the RTS noise informationrecorded on the RTS noise information recording unit 112 b of thenon-volatile memory 112 of the imaging apparatus 10.

Processing on Second Image Processing Apparatus

Next, processing executed by the second image processing apparatus 30will be described. FIG. 13 is a flowchart illustrating an outline ofprocessing executed by the second image processing apparatus 30, thatis, a flowchart of a main routine executed by the second imageprocessing apparatus 30.

First, the RTS noise correction unit 302 sets a pixel of interest to beused for sequential execution of processing in steps S702 to S705described below (step S701). The RTS noise correction unit 302 allocatesintegers greater than zero, in the raster order, such as 1, 2, 3 . . .from upper left to lower right, as an index, for each of pixels on theRAW image. Next, the RTS noise correction unit 302 increments a counterby one every time step S701 is executed (counter is reset to zero at thepoint when the processing in FIG. 13 is started). The RTS noisecorrection unit 302 sets the pixel to which the index indicated by thecounter is allocated, as a pixel of interest. That is, when step S701 isfirst executed by the RTS noise correction unit 302, the RTS noisecorrection unit 302 increments the counter, which has been reset tozero, by one. Accordingly, the counter indicates one and the pixel inthe upper left portion becomes the pixel of interest. When the RTS noisecorrection unit 302 executes processing of step S701 twice (secondtime), the counter indicates two. Accordingly, the pixel on the rightside of the upper left pixel becomes the pixel of interest.

Subsequently, the RTS noise pixel determination unit 302 a obtains RTSnoise information recorded in the RTS noise information recording unit112 b of the non-volatile memory 112 of the imaging apparatus 10 via thethird external I/F unit 301, the first external I/F unit 115, and thebus 113, and determines whether there is a possibility that RTS noise isoccurring in the pixel of interest, based on obtained RTS noiseinformation (step S702). That is, the RTS noise pixel determination unit302 a determines whether the positional information of the sharing pixelblock including the pixel of interest is included in the RTS noiseinformation. Specifically, the RTS noise pixel determination unit 302 adetermines whether the positional information of the sharing blockincluding the pixel of interest is included in the RTS noiseinformation, as a sharing block having possibility of occurrence of theRTS noise. In a case where the RTS noise pixel determination unit 302 adetermines that there is a possibility of occurrence of RTS noise on thepixel of interest (determines that the positional information of thesharing pixel block including the pixel of interest is included in theRTS noise information) (step S702: Yes), the second image processingapparatus 30 proceeds to step S703 described below. In contrast, in acase where the RTS noise pixel determination unit 302 a determines thatthere is no possibility of occurrence of RTS noise on the pixel ofinterest (determines that the positional information of the sharingpixel block including the pixel of interest is not included in the RTSnoise information) (step S702: No), the second image processingapparatus 30 proceeds to step S706 described below. In this case, in acase where the RTS noise pixel determination unit 302 a determines thatthere is no possibility that RTS noise is occurring on the pixel ofinterest, the RTS noise pixel determination unit 302 a outputs a pixelvalue of the pixel of interest on the RAW image, to the representativevalue calculation unit 302 c as a corrected pixel value, without anychange.

In step S703, the candidate value calculation unit 302 b calculates aplurality of candidate values of the correction amount for correctingthe RTS noise. Specifically, based on the RTS_Value that corresponds tothe pixel of interest (included in the RTS noise information included inthe RTS noise output from RTS noise pixel determination unit 302 a), thecandidate value calculation unit 302 b determines all values possible asthe pixel value that is not less than 0 and not greater than RTS_Value(all integers not less than 0 and not greater than RTS_Value in a casewhere solely integers are possible as RAW image) as candidate values(candidate value calculation method 1). In a case where the amplifiergain value set by the imaging controller 114 to the column amplifier, orthe like, of the image sensor 105 differs between the case of RTS noisedetection (amplifier gain value=G0) and the case of RTS noise correction(amplifier gain value=G1), the candidate value calculation unit 302 bmay replace the RTS_Value with a value obtained by multiplying the ratio(G=G1/G0) of the amplifier gain value at RTS noise correction to theamplifier gain value at RTS noise detection, by the RTS_Value. Moreover,the candidate value calculation unit 302 b may include a pre-settableRTS_Value for each of amplifier gain values into the RTS noiseinformation, and may use the RTS_Value according to the amplifier gainvalue that has been set.

Subsequently, based on the pixel value of the RAW image surrounding thepixel of interest, the representative value calculation unit 302 cexecutes representative value calculation processing of calculating arepresentative value (expected pixel value on the RAW image in a casewhere RTS noise is not occurring on pixel of interest) (step S704).After step S704, the second image processing apparatus 30 proceeds tostep S705 described below.

FIG. 14 is a flowchart illustrating an outline of representative valuecalculation processing in step S704 in FIG. 13.

As illustrated in FIG. 14, the representative value calculation unit 302c first sets a minimum calculation range as a target for calculating therepresentative value, based on the pixel of interest (step S801).Specifically, in case of determining a range of maximum of 7×7 as atarget range around the pixel of interest, as a calculation range, therepresentative value calculation unit 302 c sets a range of 3×3, thatis, a minimum range and not exceeding the 7×7 range, as a minimumcalculation range.

Subsequently, the representative value calculation unit 302 c calculatesa reference value for calculating a random noise amount that occurs inthe neighborhood of the pixel of interest on the RAW image (step S802).Specifically, the representative value calculation unit 302 ccalculates, as a reference value, the pixel value of the pixel ofinterest on the RAW image (reference value calculation method 1).

Thereafter, the random noise amount estimation unit 302 d obtains therandom noise model recorded in the non-volatile memory 112 via the thirdexternal I/F unit 301, the first external I/F unit 115, and the bus 113,and calculates a random noise amount according to the pixel value of thepixel of interest or the reference value in the neighborhood of thepixel of interest on the RAW image (step S803).

FIG. 15 is a flowchart illustrating an exemplary random noise model. InFIG. 15, the vertical axis represents the noise amount, and thehorizontal axis represents the pixel value. FIG. 15 uses the standarddeviation of the pixel value as the random noise amount on the verticalaxis, and thus, indicates the random noise model according tocharacteristic of the image sensor 105.

As illustrated in a curve L10 in FIG. 15, the greater the pixel value,the greater the random noise amount on the image sensor 105.Accordingly, the random noise amount estimation unit 302 d according tothe first embodiment calculates the random noise amount (calculatesstandard deviation) based on the random noise model of the curve L10 inFIG. 15. It may be possible to use not only the curve illustrated inFIG. 15, but also a characteristic of the random noise model obtained byapproximation with an approximate equation or polygonal lines.

Subsequently, based on the pixel value of the RAW image within thecalculation range, the representative value calculation unit 302 ccalculates an allowable range (effective range) that is a pixel valuerange available for calculating the representative value (step S804).Specifically, the representative value calculation unit 302 c calculatesthe upper limit of the allowable range (effective range) using thefollowing Formula (4).Reference Value+Random Noise Amount(standard deviation)×R+RTS_Value  (4)

where, R is a predetermined coefficient, which is set in accordance witha level how RTS noise can be visually recognized with respect to therandom noise. For example, the coefficient of R is preferably a valuearound two. Moreover, the representative value calculation unit 302 ccalculates the lower limit of the allowable range using the followingFormula (5).Reference Value−Random Noise Amount(standard deviation)×R−RTS_Value  (5)

It may be possible to use a maximum value of a plurality of candidatevalues instead of the RTS_Value. Moreover, the reference value inFormula (4) and Formula (5) may be obtained by a method different fromthat used for estimating the random noise amount by the random noiseamount estimation unit 302 d in above-described step S803. In thismanner, the representative value calculation unit 302 c is capable ofcalculating an allowable range in consideration of the RTS noise of thepixel of interest and the random noise in the neighborhood of the pixelof interest.

Thereafter, the representative value calculation unit 302 c determineswhether each of the pixel values for the pixels other than the pixel ofinterest on the RAW image within the calculation range (pixel value fora color same as the color of the pixel of interest, in case of the imagesensor 105 using a color filter) is within the allowable rangecalculated in above-described step S804, and counts the number of pixelvalues within the allowable range (step S805). The count value obtainedin step S805 tends to increase in the case of a flat subject, and tendsto decrease in the case of the subject that includes an edge. It may bepossible to omit counting for the pixel having a possibility that RTSnoise is occurring within the calculation range.

Subsequently, in a case where the count value counted in above-describedstep S805 is greater than a predetermined value ThRef (step S806: Yes),the second image processing apparatus 30 proceeds to step S809 describedbelow. The predetermined value ThRef is preferably one or more, sincethe representative value calculation unit 302 c calculates therepresentative value from the surrounding pixel of the pixel ofinterest. In contrast, in a case where the count value counted inabove-described step S805 is not greater the predetermined value ThRef(step S806: No), the second image processing apparatus 30 proceeds tostep S807 described below.

In step S807, in a case where the calculation range as a target forcalculating the representative value is maximum (step S807: Yes), thesecond image processing apparatus 30 proceeds to step S809 describedbelow. In contrast, in a case where the calculation range as a targetfor calculating the representative value is not maximum (step S807: No),the second image processing apparatus 30 proceeds to step S808 describedbelow.

In step S808, the representative value calculation unit 302 c expandsthe calculation range for calculating the representative value (stepS808). Specifically, the representative value calculation unit 302 cexpands the calculation range as a target for calculating therepresentative value by one or more pixels in the horizontal or verticaldirection, within a maximum possible range. For example, in a case wherea 3×3 range around the pixel of interest is set as the calculationrange, the representative value calculation unit 302 c resets thecalculation range to a 5×5 range around the pixel of interest. Afterstep S808, the second image processing apparatus 30 returns to stepS802. While the representative value calculation unit 302 c sets, instep S808, the 3×3 range or the 5×5 range as the calculation range, itis also possible to set, for example, a range of 5×3 or a range of 3×5as the calculation range by expanding the range solely horizontally orsolely vertically.

In step S809, the representative value calculation unit 302 c calculatesthe representative value (step S809). Specifically, the representativevalue calculation unit 302 c first selects a pixel value included in theallowable range (effective range) (pixel value for a color same as thecolor of the pixel of interest, in case of the image sensor 105 using acolor filter) among pixel values other than the pixel of interest on theRAW image, within the calculation range. Thereafter, in a case where thenumber of selected pixels is the predetermined value ThRef or above, therepresentative value calculation unit 302 c calculates (determines) amedian value of the selected pixel values, as a representative value. Ina case where the number of selected pixel values is an even number, therepresentative value calculation unit 302 c calculates the median valuecloser to the pixel value of the pixel of interest on the RAW image, asthe representative value. In this case, it is possible to avoidover-correction. Moreover, in a case where the number of selected pixelsis below the predetermined value ThRef, the representative valuecalculation unit 302 c determines, as the representative value, thepixel value of the pixel other than the pixel of interest, on the RAWimage, having a pixel value closest to the pixel value of the pixel ofinterest, on the RAW image, within the calculation range. While therepresentative value calculation unit 302 c calculates therepresentative value using the median value, it is also possible tocalculate the representative value using other methods including, forexample, an average, an intermediate value of the distribution.Moreover, the representative value calculation unit 302 c may performedge direction discernment within the calculation range and maycalculate a surrounding pixel value in the direction having the highestcorrelation, as the representative value, based on a result of the edgedirection discernment. Furthermore, the representative value calculationunit 302 c may exclude a pixel having a possibility that RTS noise isoccurring, among the pixels other than the pixel of interest within thecalculation range. At this time, in a case where there is no pixelhaving a possibility that RTS noise is occurring within the calculationrange at the point of execution of step S809, the representative valuecalculation unit 302 c determines the pixel value of the pixel ofinterest as the representative value. After step S809, the second imageprocessing apparatus 30 returns to the main routine in FIG. 13.

In this manner, the representative value calculation unit 302 ccalculates a representative value preferentially in the neighborhood ofthe pixel of interest in the above-described representative valuecalculation processing. Furthermore, in order to avoid variation of therepresentative value by edge, or the like, the representative valuecalculation unit 302 c calculates the representative value by limitingthe range of the surrounding pixels of the pixel of interest based onthe random noise amount estimated by the random noise amount estimationunit 302 d. Furthermore, it is also possible to calculate therepresentative value by excluding a neighboring pixel having apossibility that RTS noise is occurring.

Returning to FIG. 13, description of step S705 and subsequent stepsfollows below.

In step S705, the correction value calculation unit 302 e executescorrection value calculation processing of calculating a pixel value ofthe RAW image on which the RTS noise on the pixel of interest has beencorrected, based on the plurality of candidate values calculated inabove-described step S703 by the candidate value calculation unit 302 b,and based on the representative value calculated in above-described stepS704 by the representative value calculation unit 302 c. After stepS705, the second image processing apparatus 30 proceeds to step S706described below.

FIG. 16 is a flowchart illustrating an outline of correction valuecalculation processing in step S705 in FIG. 13.

As illustrated in FIG. 16, the correction value calculation unit 302 efirst determines whether the maximum value of the candidate value is athreshold or above (step S901) based on the random noise amount(standard deviation in first embodiment) estimated in above-describedstep S803 in FIG. 14 by the random noise amount estimation unit 302 d,and based on the maximum value of the candidate value calculated inabove-described step S703 in FIG. 13 by the candidate value calculationunit 302 b. Herein, the threshold is set by following Formula (6).Random Noise Amount×Rm  (6)

where, Rm is a predetermined coefficient. Rm is determined in accordancewith a level how RTS noise can be visually seen with respect to therandom noise, and the value for Rm is preferably around two. In a casewhere the correction value calculation unit 302 e determines that themaximum value of the candidate value is the threshold or above (stepS901: Yes), the second image processing apparatus 30 proceeds to stepS902 described below. In contrast, in a case where the correction valuecalculation unit 302 e determines that the maximum value of thecandidate value is not the threshold or above (step S901: No), thesecond image processing apparatus 30 proceeds to step S903 describedbelow. The correction value calculation unit 302 e may compare theRTS_Value of the pixel of interest with the threshold using theRTS_Value of the pixel of interest instead of the maximum value of thecandidate value.

In step S902, the correction value calculation unit 302 e corrects thepixel value. Specifically, the correction value calculation unit 302 ecalculates Δ using the following Formula (7).Δ=Pixel Value of Pixel of Interest on RAW Image−RepresentativeValue  (7)

Next, the correction value calculation unit 302 e compares an absolutevalue of Δ with one or more candidate values calculated inabove-described step S703 in FIG. 13 by the candidate value calculationunit 302 b, and selects a candidate value closest to the absolute valueof Δ, as δ. Since the correction value calculation unit 302 e has aplurality of candidate values being closet to the absolute value of Δ,the smallest candidate value of the plurality of candidate values is tobe selected as δ so as to avoid over-correction.

Finally, using the following Formulas (8) and (9), the correction valuecalculation unit 302 e corrects the pixel value of the pixel of intereston the RAW image by allowing the value by δ to shift in therepresentative value direction, and outputs the corrected pixel value ofthe pixel of interest, to the image processing unit 303.

In case of Δ<0,Pixel Value of Pixel of Interest on RAW Image+δ  (8)

In case of Δ≥0,Pixel Value of Pixel of Interest on RAW Image−δ  (9)

After step S902, the second image processing apparatus 30 returns to themain routine in FIG. 15. While the correction value calculation unit 302e calculates Δ and selects the smallest candidate value from among theplurality of candidate values in step S902, it is also possible tocalculate a value by individually adding or subtracting each of theplurality of candidate values onto the pixel value of the pixel ofinterest on the RAW image, and to select a closest representative valueamong the plurality of values after addition or subtraction, obtainedafter the calculation. Moreover, in a case where the correction valuecalculation unit 302 e can obtain the same result in step S902, anothercalculation method or comparison method may be employed. Still further,processing executed by the correction value calculation unit 302 e isequivalent to processing of determining a representative value clippedonto a range not less than the pixel value of the pixel of interest onthe RAW image−RTS_Value and not greater than the pixel value of thepixel of interest on the RAW image+RTS_Value, as the corrected pixelvalue of the pixel of interest.

In step S903, the correction value calculation unit 302 e outputs thepixel value of the pixel of interest on the RAW image, to the imageprocessing unit 303, without any change. After step S903, the secondimage processing apparatus 30 returns to the main routine in FIG. 15.

Returning to FIG. 13, description of step S706 and subsequent stepsfollows below.

In step S706, the RTS noise correction unit 302 determines whetherprocessing in above-described steps S701 to S705 is finished for all thepixels (step S706). In a case where the RTS noise correction unit 302determines that the above-described processing is finished for all thepixels (step S706: Yes), the second image processing apparatus 30finishes the current processing. In contrast, in a case where the RTSnoise correction unit 302 determines that the above-described processingis not finished for all the pixels (step S706: No), the second imageprocessing apparatus 30 returns to above-described step S701.

According to the above-described first embodiment of the presentinvention, the pixel value group creation unit 202 b creates theplurality of pixel value groups using classification into each of theread-out circuits (amplifier units 105 e), making it possible to detectRTS noise with high accuracy without increasing the number of dark-timeRAW images. As a result, it is possible to reduce the time to shootdark-time RAW image for detection, the time to read the dark-time RAWimage at detection, or the like, and it is possible to detect, with highaccuracy, the blinking defect noise with a pixel value varying within afixed range, such as the RTS noise, while reducing the time needed fordetecting the RTS noise.

Moreover, according to the first embodiment of the present invention,the RTS noise feature data calculation unit 202 d uses, in detection ofRTS noise, a histogram in which individual values in the pixel valuegroup are converted into absolute values. This can enhance accuracy ofdistribution and reduce the range of distribution to a half, and thus,can reduce the calculation amount in error calculation by fitting, toabout a half of the case where the absolute value is not used.Accordingly, it is possible to detect, with high speed and highaccuracy, the noise with a pixel value varying within a fixed range,such as the RTS noise.

Furthermore, according to the first embodiment of the present invention,since the pixel value group creation unit 202 b excludes the isolatedpoints and pixels detected by the isolated point detection unit 202 a indetection of RTS noise, it is possible to avoid false detection of RTSnoise due to occurrence of the isolated point. Accordingly, it ispossible to detect, with high accuracy, the noise with a pixel valuevarying within a fixed range, such as the RTS noise.

Still further, according to the first embodiment of the presentinvention, whether there is a possibility that RTS noise is occurring isdetermined by using the noise level of the pixel value group in RTSnoise detection. Accordingly, it is possible to reduce the calculationamount since there is no need to calculate RTS noise feature data byfitting, or the like for all the pixel value groups.

Moreover, according to the first embodiment of the present invention,the correction value calculation unit 302 e uses a plurality ofcandidate values calculated by the candidate value calculation unit 302b in the RTS noise correction, whereby it is possible to appropriatelycorrect the noise with a pixel value varying within a fixed range, suchas the RTS noise, while avoiding over-correction in the case ofoccurrence of the RTS noise below RTS_Value.

Furthermore, according to the first embodiment of the present invention,the correction value calculation unit 302 e calculates a representativevalue from appropriate surrounding pixels based on the random noiseamount estimated by the random noise amount estimation unit 302 d, inthe RTS noise correction, it is possible to minimize effects of edgeeven when the pixel of interest is in the neighborhood of the edgeportion and appropriately correct the noise with a pixel value varyingwithin a fixed range, such as the RTS noise.

Furthermore, according to the first embodiment of the present invention,in a case where the RTS noise is unnoticeably hidden by the random noisein RTS noise correction, the correction value calculation unit 302 edoes not perform processing of correcting the RTS noise. Accordingly, itis possible to appropriately correct the noise with a pixel valuevarying within a fixed range, such as the RTS noise, while avoidingover-correction.

First Modification of First Embodiment

The RTS noise feature data calculation unit 202 d according to FirstModification of the first embodiment of the present invention is capableof calculating the RTS_Value with another calculation method.Specifically, it may be possible to consider the absolute valuehistogram (blinking defect noise model) in above-described FIG. 11 to bea mixed normal distribution including three normal distributions havingdifferent characteristics as illustrated in FIG. 17, and obtain a medianvalue and standard deviation of each of the normal distributions suchthat the difference between the absolute value histogram and the mixednormal distribution becomes minimum and obtain the proportion of threedistributions so as to calculate the median value of distribution 2 asRTS_Value. In this case, the RTS noise feature data calculation unit 202d can decrease the calculation amount using a known EM algorithm, or thelike.

Second Modification of First Embodiment

The candidate value calculation unit 302 b according to SecondModification of the first embodiment of the present invention calculatesa plurality of candidate values of the correction amount for correctingthe RTS noise (step S703 in FIG. 13 described above) using anothercandidate value calculation method (candidate value calculation method2).

Candidate Value Calculation Method 2

The candidate value calculation unit 302 b calculates a value extractedat a predetermined interval from values not less than 0 and not greaterthan RTS_Value, as a candidate value, based on RTS_Value thatcorresponds to the pixel of interest. For example, an average standarddeviation×r×n (n is integer not less than 0) that is not greater thanRTS_Value is calculated as the candidate value using an average standarddeviation as a value based on the average random noise amount. Note thatr is determined in accordance with a level how RTS noise can be visuallyseen with the respect to the random noise. Preferably, r is a valuearound two.

Moreover, the candidate value calculation unit 302 b may calculate amaximum value of RTS_Value/(Max−1)×n (n is integer not less than 0 andnot greater than Max−1) that is not greater than RTS_Value, as thecandidate value, based on the maximum value of the RTS_Value and amaximum number Max of the candidate value determined from hardwarerestrictions (restrictions including memory and circuit scale).

FIG. 18 is a diagram illustrating a relationship between RTS_Valuehistogram in the candidate value calculation method 2 and a candidatevalue. As illustrated in FIG. 18, a thick line L11 represents anexemplary histogram of the RTS_Value. In a case where the maximum numberMax is seven, the value represented by a dotted line is a possiblecandidate value, and an actual candidate value is a value not greaterthan RTS_Value corresponding to the pixel of interest, represented by adotted line.

According to above-described Second Modification of the firstembodiment, the candidate value calculation unit 302 b calculates aplurality of candidate values of the correction amount for correctingthe RTS noise in consideration of hardware restriction. This enableseffective correction on the RTS noise occurred in the image sensor 105.

Third Modification of First Embodiment

The candidate value calculation unit 302 b according to ThirdModification of the first embodiment of the present invention calculatesa plurality of candidate values of the correction amount for correctingthe RTS noise (step S703 in FIG. 13 described above) using anothercandidate value calculation method (candidate value calculation method3).

Candidate Value Calculation Method 3

The candidate value calculation unit 302 b divides RTS_Value histogramfrequency equally in accordance with the maximum number Max of thecandidate values determined by hardware restriction, and calculates thevalue not greater than RTS_Value corresponding to the pixel of interest,as the candidate value.

FIG. 19 is a diagram illustrating a relationship between RTS_Valuehistogram in the candidate value calculation method 3 and a candidatevalue. As illustrated in FIG. 19, a thick line L12 represents anexemplary histogram of the RTS_Value. The value represented by thedotted line indicates a possible candidate value, being the positionthat equally divides the RTS_Value histogram frequency. The actualcandidate value is a value not greater than RTS_Value corresponding tothe pixel of interest, represented by a dotted line.

In this manner, according to Third Modification of the first embodiment,the candidate value calculation unit 302 b calculates a candidate valueof the correction amount for correcting the RTS in view of thedistribution of the RTS noise, taking hardware restriction inconsideration. This enables effective and highly accurate correction tobe performed on all RTS noise occurred in the image sensor 105.

Fourth Modification of First Embodiment

The candidate value calculation unit 302 b according to FourthModification of the first embodiment of the present invention calculatesa plurality of candidate values of the correction amount for correctingthe RTS noise (step S703 in FIG. 13 described above) using anothercandidate value calculation method (candidate value calculation method4).

Candidate Value Calculation Method 4

The candidate value calculation unit 302 b calculates a value extractedat a predetermined interval from values that are not less than 0 and notgreater than RTS_Value, as a candidate value, based on RTS_Value thatcorresponds to the pixel of interest. The difference from the candidatevalue calculation methods 2 and 3 is that the predetermined interval ischanged in accordance with the pixel value. Specifically, the candidatevalue calculation unit 302 b calculates a candidate value correspondingto the pixel of interest based on the random noise amount (standarddeviation in Fourth Modification of first embodiment) according to apixel value of the pixel of interest or a pixel value on the RAW imagein the neighborhood of the pixel of interest, calculated by the randomnoise amount estimation unit 302 d. More specifically, the candidatevalue calculation unit 302 b obtains the random noise calculated by therandom noise amount estimation unit 302 d based on the average of thepixel value of the pixel of interest on the RAW image and the pixelvalue on the RAW image neighboring (surrounding) the pixel of interest(pixel value for a color same as the color of the pixel of interest, incase of the image sensor 105 using a color filter), or the like, andthen, using the random noise amount, the candidate value calculationunit 302 b calculates standard deviation×r×n (n is integer not less than0 and not greater than Max−1), within a range of the RTS_Value of below,as a candidate value.

According to above-described Fourth Modification of the firstembodiment, the candidate value calculation unit 302 b calculates thecandidate value corresponding to the pixel of interest based on therandom noise amount according to the pixel value of the pixel ofinterest or the pixel value on the RAW image in the neighborhood of thepixel of interest, calculated by the random noise amount estimation unit302 d. Performing correction with fine accuracy, for a case possiblyunnoticeably hidden by the random noise, would increase the calculationamount without achieving effects of correction that meets the increasein the calculation amount. Using the technique in Fourth Modification ofthe first embodiment, however, it is possible to sufficiently correctthe RTS noise of a level having strangeness that is not hidden by therandom noise, with a minimum possible number of candidate values.

Fifth Modification of First Embodiment

The representative value calculation unit 302 c according to FifthModification of the first embodiment of the present invention calculatesa reference value (above-described step S802 in FIG. 14) for calculatingthe random noise amount that occurs in the neighborhood of the pixel ofinterest on the RAW image, using another reference value calculationmethod (reference value calculation method 2).

Reference Value Calculation Method 2

The representative value calculation unit 302 c calculates any of amaximum value, an average, and a median value, of the pixel values onthe RAW image within a calculation range (pixel value for a color sameas the color of the pixel of interest, in case of the image sensor 105using a color filter), as the reference value.

According to the above-described Fifth Modification of the firstembodiment, in a case where the pixel value on the RAW image is smallerthan the original pixel value due to RTS noise, the representative valuecalculation unit 302 c calculates a reference value using a pixel valuesurrounding the pixel of interest. Accordingly, it is possible toprevent the random noise amount calculated by the random noise amountestimation unit 302 d from being calculated as a value smaller than theactual value.

Sixth Modification of First Embodiment

The representative value calculation unit 302 c according to SixthModification of the first embodiment of the present invention calculatesa reference value (above-described step S802 in FIG. 14) for calculatingthe random noise amount that occurs in the neighborhood of the pixel ofinterest on the RAW image, using another reference value calculationmethod (reference value calculation method 3).

Reference Value Calculation Method 3

The representative value calculation unit 302 c calculates, within acalculation range, any of a maximum value, an average, and a medianvalue, of the pixel values, on the RAW image, of the pixel determined toinclude no occurrence of RTS noise by the RTS noise pixel determinationunit 302 a (pixel value for a color same as the color of the pixel ofinterest, in case of the image sensor 105 using a color filter), as thereference value. In a case where the RTS noise pixel determination unit302 a determines that RTS noise is occurring on all the pixels within acalculation range, the representative value calculation unit 302 cperforms calculation using the above-described reference valuecalculation method 1 or reference value calculation method 2.

According to the above-described Sixth Modification of the firstembodiment of the present invention, in a case where the pixel value onthe RAW image is smaller than the original pixel value due to RTS noise,the representative value calculation unit 302 c calculates a referencevalue using a pixel value surrounding the pixel of interest.Accordingly, it is possible to prevent the random noise amountcalculated by the random noise amount estimation unit 302 d from beingcalculated as a value smaller than the actual value.

Seventh Modification of First Embodiment

The representative value calculation unit 302 c according to SeventhModification of the first embodiment of the present invention calculatesa reference value for calculating the random noise amount that occurs inthe neighborhood of the pixel of interest on the RAW image, usinganother reference value calculation method (reference value calculationmethod 4).

Representative Value Calculation Method 4

The representative value calculation unit 302 c calculates the randomnoise amount using the representative value by the random noise amountestimation unit 302 d, and determines a threshold in above-describedstep S901 using the random noise amount based on the representativevalue instead of the reference value.

According to Seventh Modification of the first embodiment of the presentinvention, it is possible to prevent the threshold from being calculatedas a smaller or larger value than the actual value in a case where RTSnoise is occurring on the pixel of interest.

Eighth Modification of First Embodiment

In the first embodiment the present invention, the average random noiseamount calculation unit 202 c calculates the random noise amount of eachof the plurality of pixel value groups and calculates the average of theaverage random noise amounts of all the pixel value groups for which theRTS flag is set to the clear state.

Alternatively, the average random noise amount calculation unit 202 cmay calculate a random noise amount of each of the plurality of pixelvalue groups, and may calculate the random noise amount of the pixelthat corresponds to the pixel value group for which the RTS flag is setto a clear state on a plurality of dark-time RAW images, as the randomnoise amount of the plurality of dark-time RAW images. In this case, theRTS noise determination unit 202 e may determine whether RTS noiseoccurs on each of the plurality of pixel value groups based on therandom noise amount of each of the plurality of pixel value groupscalculated by the average random noise amount calculation unit 202 c,the random noise amount of the plurality of dark-time RAW imagescalculated by the average random noise amount calculation unit 202 c,and the feature data calculated by the RTS noise feature datacalculation unit 202 d. This enables calculation of the random noiseamount of the plurality of dark-time RAW images with higher accuracy. Asa result, it is possible to determine with higher accuracy whether it isa pixel value group having a possibility of occurrence of the RTS noise.

Second Embodiment

Next, a second embodiment of the present invention will be described. Inthe first embodiment, the RTS_Value is recorded when detecting the RTSnoise, and a plurality of candidate values of the correction amount iscalculated based on the recorded RTS_Value. In contrast, the secondembodiment first calculates all possible numbers to be the candidatevalue of the correction amount shared by the whole image in use, as aresult of RTS noise detection. Next, correction is performed on asharing pixel block on which RTS noise occurs, using from a smallestvalue as a candidate value of the correction amount among all thenumbers possible as the candidate value of the correction amount, whilethe number of the candidate values of the correction amount is recorded.Specifically, the difference from the above-described first embodimentis in processing of step S609 in RTS noise determination processing inFIG. 12 and processing of step S703 in FIG. 13. Accordingly,hereinafter, processing in steps S609 and S703 according to the secondembodiment will be described. The same reference signs are used todesignate the same elements as those in the first embodiment, andexplanation thereof will be omitted.

FIG. 20 is a flowchart illustrating outline of RTS noise determinationprocessing executed by the first image processing apparatus 20 accordingto the second embodiment. In FIG. 20, the first image processingapparatus 20 performs processing of step S609 a instead of theprocessing of S609 according to the above-described first embodiment.

In step S609 a, the RTS noise determination unit 202 e first calculatesa maximum value of the RTS_Value temporarily stored in the volatilememory 111 in above-described step S605.

Thereafter, the RTS noise determination unit 202 e calculates Cn (n isinteger not less than zero and less than Max) using the followingFormula (10), based on the maximum number Max of the candidate value.Cn=Maximum Value of RTS_Value/(Max−1)×n  (10)

where n is integer not less than 0 and less than Max. In above-describedFIG. 18, Cn calculated by Formula (10) corresponds to the position ofthe dotted line.

Subsequently, the RTS noise determination unit 202 e counts the numberof Cn that is not greater than RTS_Value, based on the RTS_Valuetemporarily stored in the volatile memory 111 in association with theposition of the sharing pixel block. In this case, since C0 (=0) isalways included, the RTS noise determination unit 202 e may exclude thisfrom the count value. Moreover, the RTS noise determination unit 202 emay count the number up to the minimum Cn exceeding the RTS_Value.

Finally, the RTS noise determination unit 202 e records RTS noiseinformation into the RTS noise information recording unit 112 b. The RTSnoise information associates the number of maximum numbers Max,individual Cn, positional information of the sharing pixel blocks, andthe count value (number of candidate values of the RTS noise correctionvalue) with each other. Other than the above-described values, the RTSnoise determination unit 202 e may use a value indicated in dotted linein the above-described FIG. 19, as Cn. In this case, Cn is a valueobtained by equally dividing the frequency of the histogram ofRTS_Value, by maximum value Max−1.

FIG. 21 is a flowchart illustrating an outline of processing executed bythe second image processing apparatus 30 according to the secondembodiment. In FIG. 21, the second image processing apparatus 30performs processing of step S703 a instead of the processing of S703according to the above-described first embodiment.

In step S703 a, the candidate value calculation unit 302 b obtains thenumber of maximum number Max and Cn recorded in the non-volatile memory112, and determines the number of candidate values corresponding to thepixel of interest from smaller Cn, as the candidate value of thecorrection amount. In a case where the amplifier gain value set by theimaging controller 114 to the amplifier gain value set to the columnamplifier, or the like, of the image sensor 105 differs between the timeof RTS noise detection (amplifier gain value=G0) and the time of RTSnoise correction (amplifier gain value=G1), the candidate valuecalculation unit 302 b may replace the candidate value of the correctionamount with a value obtained by multiplying the ratio (G=G1/G0) of theamplifier gain value at RTS noise correction to the amplifier gain valueat RTS noise detection, by the candidate value of the correction amount.Moreover, the candidate value calculation unit 302 b may recordbeforehand the candidate value of the correction amount for each of theamplifier gain values, in the non-volatile memory 112, and may use thecandidate value of the correction amount according to the set amplifiergain value. Moreover, in a case where zero is not included in the numberof candidate values, the candidate value calculation unit 302 bdetermines the number of candidate values of C0 and smaller value fromC1 or above, as the candidate value of the correction amount. Stillfurther, the above-described first embodiment includes processing thatuses RTS_Value. In the second embodiment, however, the RTS_Value is notrecorded in the non-volatile memory 112, and thus, by using the maximumcandidate value calculated in step S703 a as the RTS_Value in the laterprocessing, it is possible to achieve an effect similar to the effectsof the above-described first embodiment.

According to the above-described second embodiment of the presentinvention, in RTS noise detection, possible values (Cn) for thecandidate value of the correction amount in the RTS noise correction arecalculated beforehand based on the distribution of the RTS_Value, in RTSnoise detection, and the sharing pixel block in which RTS noise occursand the number of candidate values to be used in RTS noise correctionare associated with each other based on RTS_Value, and recorded. The RTSnoise occurs due to a defect in the image sensor 105, and thus,individual differences lead to difference in the RTS noisecharacteristics. In a case where the RTS_Value is directly recorded asin the above-described first embodiment, there is a need to ensure anarea (maximum bit number of RTS_Value) for recording possible assumablemaximum value of RTS_Value, obtained in consideration of individualdifference. In contrast, according to the second embodiment, it issufficient to record the value up to the maximum number Max. According,it is possible to perform detection that leads to appropriate correctioneven in a case where unexpected RTS noise occurs. In a case whereunexpected RTS noise occurs, it is only required to change Cn alone.

Moreover, according to the second embodiment of the present invention,it is only required, in RTS noise correction, to perform selection fromCn by the number of candidate values, using previously calculated Cn andusing the candidate value that is a value based on RTS_Value associatedwith the sharing pixel block. Accordingly, it is possible to performcorrection with simple processing, that is, small calculation amount.

While in the second embodiment of the present invention, the candidatevalue calculation unit 302 b calculates Cn based on RTS noise detectionresults, it is also possible to calculate (determine) Cn beforehandbased on a characteristic (for example, average RTS_Value histogram) ofthe RTS noise that might occur on the image sensor 105 in a case wherethere is a small individual difference in the characteristic (RTS_Valuehistogram, in particular) of the RTS noise. As a result, there is noneed to calculate Cn for each of the individuals (each of the imagesensors 105), and thus, can reduce the calculation amount in RTS noisedetection.

Third Embodiment

Next, a third embodiment of the present invention will be described. TheRTS noise detection unit 202 and the RTS noise correction unit 302 areseparately provided in the above-described first embodiment. In thethird embodiment, however, the RTS noise detection unit 202 and the RTSnoise correction unit 302 are provided on an imaging apparatus mainbody. Therefore, the same reference signs are used to designate the sameelements as those of the imaging system 1 according to the firstembodiment, and explanation thereof will be omitted.

Configuration of Imaging System

FIG. 22 is a block diagram schematically illustrating a configuration ofan imaging system 2 according to a third embodiment of the presentinvention. The imaging system 2 illustrated in FIG. 22 includes a mainbody unit 3, a lens unit 4 that can be connected removably with the mainbody unit 3.

Configuration of Main Body Unit

The main body unit 3 includes the shutter 103, the image sensor 105, theanalog processing unit 106, the A/D converter 107, the operating unit108, the memory I/F unit 109, the recording medium 110, the volatilememory 111, the non-volatile memory 112, the bus 113, the imagingcontroller 114, an AE processing unit 116, an AF processing unit 117, anexternal I/F unit 118, a display unit 119, a driver 120, the RTS noisedetection unit 202, and the RTS noise correction unit 302. The driver120 drives the shutter 103 under the control of the imaging controller114.

The AE processing unit 116 obtains image data stored in the volatilememory 111 via the bus 113, and sets exposure conditions at the time ofperforming still image shooting or moving image shooting based on theobtained image data. Specifically, the AE processing unit 116 calculatesluminance from the image data and determines a diaphragm value, exposuretime, ISO sensitivity, or the like, based on the calculated luminance,thereby performing automatic exposure (Auto Exposure) for the imagingsystem 2.

The AF processing unit 117 obtains the image data stored in the volatilememory 111 via the bus 113, and performs automatic focus adjustment ofthe imaging system 2 based on the obtained image data. For example, theAF processing unit 117 extracts a high-frequency component signal fromthe image data, and performs auto-focus (AF) calculation processing ontothe high-frequency component signal, thereby determining focusingevaluation of the imaging system 2, thereby performing automatic focusadjustment of the imaging system 2. A method for performing automaticfocus adjustment of the imaging system 2 may be a method of obtaining aphase difference signal on the image sensor 105.

The external I/F unit 118 is capable of performing data read/write onvarious blocks of the main body unit 3 and control using dedicatedcommands, or the like. The external I/F unit 118 is an interface capableof controlling various blocks on the main body unit 3 by connecting adedicated circuit that includes FPGA, DSP, GPU, or the like, and anexternal device such as a personal computer (PC).

The display unit 119 includes a display panel formed with liquidcrystal, organic electroluminescence (EL), or the like. The display unit119 displays an image that corresponds to the image data generated bythe image sensor 105.

Configuration of Lens Unit

As illustrated in FIG. 22, the lens unit 4 includes the optical system101, the diaphragm 102, and the driver 104. The optical system 101focuses a subject image collected from a predetermined view-field area,onto the image sensor 105.

Processing on Imaging System

Next, processing executed by the imaging system 2 will be described.FIG. 23 is a flowchart illustrating outline of processing executed bythe imaging system 2.

As illustrated in FIG. 23, a power button (not illustrated) of theoperating unit 108 is first operated and then the power supply of themain body unit 3 is turned on, thereafter, the imaging controller 114initializes the imaging system 2 (step S1001). Specifically, the imagingcontroller 114 executes initialization of turning the recording flagindicating recording of the moving image, to the off-state. Thisrecording flag is a flag that turns into an on-state during moving imageshooting and into an off-state when no moving image is not being shot,being stored in the volatile memory 111.

Subsequently, in a case where the moving image button on the operatingunit 108 is pressed (step S1002: Yes), the imaging controller 114reverses the recording flag indicating that a moving image is beingrecorded in the on-state (step S1003), and the imaging controller 114judges whether the imaging system 2 is in the middle of recording amoving image (step S1004). Specifically, the imaging controller 114determines whether the recording flag stored in the volatile memory 111is in the on-state. In a case where the imaging controller 114 judgesthat the imaging system 2 is in the middle of recording the moving image(step S1004: Yes), the imaging system 2 proceeds to step S1005 describedbelow. In contrast, in a case where the imaging controller 114 judgesthat the imaging system 2 is not in the middle of recording the movingimage (step S1004: No), the imaging system 2 proceeds to step S1006described below.

In step S1005, the imaging controller 114 generates, on the recordingmedium 110, a moving image file for recording image data in the timeseries. After step S1005, the imaging system 2 proceeds to step S1006described below.

In step S1002, in a case where the moving image button on the operatingunit 108 is not pressed (step S1002: No), the imaging system 2 proceedsto step S1006.

Subsequently, the imaging controller 114 judges whether the imagingsystem 2 is in the middle of recording a moving image (step S1006). In acase where the imaging controller 114 judges that the imaging system 2is in the middle of recording the moving image (step S1006: Yes), theimaging system 2 proceeds to step S1017 described below. In contrast, ina case where the imaging controller 114 judges that the imaging system 2is not in the middle of recording the moving image (step S1006: No), theimaging system 2 proceeds to step S1007 described below.

In a case where, in step S1007, a playback button on the operating unit108 is pressed (step S1007: Yes), the imaging system 2 plays back anddisplays an image that corresponds to the image data recorded in therecording medium 110, on the display unit 119 (step S1008). After stepS1008, the imaging system 2 proceeds to step S1009 described below.

In step S1007, in a case where the playback button on the operating unit108 is not pressed (step S1007: No), the imaging system 2 proceeds tostep S1009.

Subsequently, in a case where a menu button on the operating unit 108 ispressed (step S1009: Yes), the imaging system 2 executes settingprocessing of performing various settings (step S1010). Details of thesetting processing will be described below. After step S1010, theimaging system 2 proceeds to step S1011 described below.

In step S1009, in a case where the menu button on the operating unit 108is not pressed (step S1009: No), the imaging system 2 proceeds to stepS1011.

In step S1011, in a case where the release button on the operating unit108 is transitioned from the off-state to a 1st state (step S1011: Yes),the imaging controller 114 causes the AE processing unit 116 to executeAE processing of adjusting exposure and causes the AF processing unit117 to execute AF processing of adjusting focus (step S1012).Thereafter, the imaging system 2 proceeds to step S1024 described below.

In a case where, in step S1011, the release button on the operating unit108 has not transitioned from the off-state to the 1st state (stepS1011: No), the imaging system 2 proceeds to step S1013.

Subsequently, in a case where the release button of the operating unit108 has transitioned to the 2nd state (step S1013: Yes), the imagingcontroller 114 executes shooting using a mechanical shutter (stepS1014). Specifically, the imaging controller 114 causes the image sensor105 to execute shooting by controlling the shutter 103.

Subsequently, the imaging system 2 executes image processing, that is,first performs RTS noise correction onto the image data generated by theimage sensor 105, and thereafter, performs predetermined processing(step S1015). Details of the image processing will be described below.

Thereafter, the imaging controller 114 records the image data that hasundergone image processing by the image processing unit 303, on therecording medium 110 (step S1016). After step S1016, the imaging system2 proceeds to step S1024 described below.

In step S1013, in a case where the release button of the operating unit108 has not transitioned to the 2nd state (step S1013: No), the imagingsystem 2 proceeds to step S1017.

Subsequently, the imaging controller 114 causes the AE processing unit116 to execute AE processing of adjusting exposure (step S1017), andcauses the AF processing unit 117 to execute AF processing of adjustingthe focus (step S1018).

Thereafter, the imaging controller 114 causes the image sensor 105 toelectronically control the exposure time, that is, to execute shootingusing an electronic shutter, via the driver 120 (step S1019). The imagedata generated with the electronic shutter by the image sensor 105 areoutput to the volatile memory 111 via the analog processing unit 106,the A/D converter 107, and the bus 113.

Subsequently, the imaging system 2 executes image processing similar tothe processing in step S1015 (step S1020). Details of the imageprocessing will be described below.

Thereafter, the imaging system 2 displays a live-view image thatcorresponds to the image data generated with shooting with theelectronic shutter by the image sensor 105, on the display unit 119(step S1021).

Subsequently, in a case where the imaging system 2 is in the middle ofrecording a moving image (step S1022: Yes), the imaging controller 114causes an image compressing/developing section (not illustrated) tocompress the image data into a recording format set by settingprocessing in step S1010, and records the compressed image data as amoving image in a moving image file created on the recording medium 110(step S1023). After step S1023, the imaging system 2 proceeds to stepS1024.

In step S1022, in a case where the imaging system 2 is not in the middleof recording the moving image (step S1022: No), the imaging system 2proceeds to step S1024.

Subsequently, in a case where a power button on the operating unit 108is pressed and the power of the imaging system 2 turns into theoff-state (step S1024: Yes), the imaging system 2 finishes the currentprocessing. In contrast, in a case where the power of the imaging system2 is not in an off-state (step S1024: No), the imaging system 2 returnsto step S1002.

Next, setting processing described in step S1010 in FIG. 23 will bedescribed. FIG. 24 is a flowchart illustrating an outline of the settingprocessing.

As illustrated in FIG. 24, the imaging system 2 displays a menu on thedisplay unit 119 (step S1041).

Subsequently, in a case where detection of RTS noise is selected inresponse to the operation on the operating unit 108 (step S1042: Yes),the RTS noise detection unit 202 executes RTS noise detection processing(step S1043). Herein, since the RTS noise detection processingcorresponds to the processing executed by the first image processingapparatus 20 according to the above-described first embodiment,description thereof will be omitted. After step S1043, the imagingsystem 2 proceeds to step S1044.

In a case where, in step S1042, detection of RTS noise in response tothe operation on the operating unit 108 is not selected (step S1042:No), the imaging system 2 proceeds to step S1044.

Subsequently, in a case where the menu button on the operating unit 108is pressed (step S1044: Yes), the imaging system 2 returns to the mainroutine in FIG. 23. In contrast, in a case where the menu button on theoperating unit 108 is not pressed (step S1044: No), the imaging system 2returns to above-described step S1042.

Next, image processing described in steps S1015 and S1020 in FIG. 23will be described. FIG. 25 is a flowchart illustrating an outline ofimage processing.

As illustrated in FIG. 25, the RTS noise correction unit 302 executesRTS noise correction processing of correcting RTS noise on the imagedata generated by the image sensor 105 (step S1051). The RTS noisecorrection processing corresponds to the processing executed by thesecond image processing apparatus 30 according to the above-describedfirst embodiment, description thereof will be omitted.

Subsequently, the image processing unit 303 executes basic imageprocessing onto the image data on which the RTS noise correction unit302 has corrected the RTS noise (step S1052). After step S1052, theimaging system 2 returns to the main routine in FIG. 23.

With the above-described third embodiment of the present invention,effects similar to the effects of the above-described first embodimentcan be achieved.

First Modification of First to Third Embodiments

In the above-described first to third embodiments the present invention,the RTS noise is corrected by calculating a plurality of candidatevalues. While the number of the plurality of candidate values is notparticularly limited in the above description, it implies there is aplurality of candidate values substantially different from each other.For example, in a case where the RTS noise correction unit 302 isimplemented by hardware in the first to third embodiments the presentinvention, circuit design would be simpler by using a fixed number forthe plurality of candidate values.

Specifically, it would be sufficient to prepare a memory for storing aplurality of candidate values for the above-described maximum numberMax, and set a same value to the plurality of memories if the number ofthe plurality of candidate values is below Max. In this case, the numberof candidate values stored in the memory is Max, while the number ofsubstantially different candidate values is below Max. For example, inthe above-described second embodiment of the present invention, in acase where the count value of the sharing blocks including the pixel ofinterest is two (when zero is not counted) and Max is seven, the circuitmay be configured such that the following values are set in the memory:

C0=0

C1

C2

C2

C2

C2

C2

In another case where the count value of the sharing blocks includingthe pixel of interest is six (when zero is not counted), the circuit maybe configured such that the following values are set in the memory:

C0=0

C1

C2

C3

C4

C5

C6

According to above-described First Modification of the first to thirdembodiments of the present invention, implementation of the circuit ispossible such that an appropriate correction amount is always selectedfrom a fixed number of (seven in the example above) candidate valueswithout changing the target for selection depending on each of the countvalues.

Moreover, according to First Modification of the first to thirdembodiments of the present invention, even in a case where a maximumvalue of the candidate value is used instead of the RTS_Value of thepixel of interest, it would be sufficient to always refer to thecandidate value on a same position in the above-described example(seventh position in the above-described example).

Second Modification of First to Third Embodiments

In the above-described first to third embodiments of the presentinvention, the RTS noise determination unit 202 e may determine order ofpriority for RTS noise information and record the RTS noise informationin the RTS noise information recording unit 112 b. For example, the RTSnoise determination unit 202 e determines order of priority for the RTSnoise information using one of (A) to (F) described below, and recordsthe RTS noise information in the RTS noise information recording unit112 b.

Raster order . . . (A)

In order of proximity to image center portion . . . (B)

RTS_Value in descending order . . . (C)

The number of candidates of correction amount in descending order . . .(D)

Frequency of occurrence of RTS noise in descending order . . . (E)

Occurrence density (denseness) of RTS noise in descending order . . .(F)

The method for determining the order of priority is not limited to, butthe above-described (A) to (F) may be appropriately combined. Forexample, the RTS noise determination unit 202 e may determine the orderof priority: the order of proximity to image center portion(above-described (B)) as first priority; and RTS_Value in descendingorder (above-described (C)) or the number of candidates of correctionamount in descending order (above-described (D)) as second priority. Ifthe distance from the image center portion is the same, the RTS noisedetermination unit 202 e may prioritize RTS_Value in descending order orthe number of candidates of correction amount in descending order torecord the RTS noise information in the RTS noise information recordingunit 112 b.

According to the above-described Second Modification of the first tothird embodiments of the present invention, the RTS noise determinationunit 202 e prioritizes the RTS noise easily noticeable depending onusage situation, and records the RTS noise information in the RTS noiseinformation recording unit 112 b even in a case where there is an upperlimit in the number of RTS noise information items recordable on the RTSnoise information recording unit 112 b. This configuration enablesprioritized correction of the RTS noise easily noticeable depending onusage situation.

Moreover, in a case where there is limitation beforehand on the changeof the image expansion rate and change of the display position in theabove-described Second Modification of the first to third embodiments ofthe present invention, the RTS noise determination unit 202 e may changethe priority in accordance with the expansion rate or the displayposition, and may record the RTS noise information associated with theexpansion rate or the display position, in the RTS noise informationrecording unit 112 b. In this case, the RTS noise correction unit 302may read the RTS noise information associated with the set expansionrate or the set display position and may perform correction processing.

Other Embodiments

The present invention is not limited to the above-described embodiments,but various modifications and further applications are of courseavailable within the scope of the present invention. For example,besides the imaging apparatus used in the description of the presentinvention, the present invention can be applied to any apparatus capableof shooting a subject such as a mobile apparatus having an image sensorof a mobile phone or a smartphone or an imaging apparatus that imagesthe subject by using an optical device such as a video camera, anendoscope, a surveillance camera, or a microscope.

Moreover, the present invention is applicable to image data other thanthe image data used for display and recording, for example, applicableto the image data in an OB area, and image data in an area outside theimage circle, with no optical design assurance.

Moreover, in the description of the flowcharts for the operationsdescribed above in the present specification, terms such as “first”,“next”, “subsequently”, “thereafter” are used to describe operation forconvenience. These do not denote, however, that the operations need tobe performed in this order.

Moreover, the methods of the processing performed by the imageprocessing apparatus in the above-described embodiments, that is, any ofthe processing illustrated in the flowcharts may be stored as a programthat can be executed by a control unit such as a CPU. In addition, it ispossible to distribute by storing in a storage medium of the externalstorage device, such as memory cards (ROM card, RAM card, etc.), amagnetic disk (floppy disk, hard disk, etc.), an optical disc (CD-ROM,DVD, etc.), a semiconductor memory. The control unit such as a CPU readsthe program stored in the storage medium of the external storage deviceand controls the operation by the read program to execute theabove-described processing.

According to some embodiments, it is possible to perform high-accuracydetection of blinking defect noise that varies within a fixed range,such as the RTS noise, and to reduce the shooting time and thecalculation time.

The present invention is not limited to the above-described embodimentsand modification examples just as they are but can be embodied bymodifying the elements without departing from the scope of the inventionat a stage of implementation of the invention. Furthermore, a pluralityof elements disclosed in the above-described embodiments may beappropriately combined to form various inventions. For example, someelements may be omitted from the all the elements described in theembodiments and the modification example. Furthermore, the elementsdescribed in each of exemplary embodiments and modification examples maybe appropriately combined with each other.

Additional advantages and modifications will readily occur to thoseskilled in the art. Therefore, the invention in its broader aspects isnot limited to the specific details and representative embodiments shownand described herein. Accordingly, various modifications may be madewithout departing from the spirit or scope of the general inventiveconcept as defined by the appended claims and their equivalents.

What is claimed is:
 1. An image processing apparatus comprising: a pixelvalue group creation unit configured to create, for a plurality ofpieces of image data generated by an image sensor including: a pluralityof pixels arranged two-dimensionally to receive light from outside andgenerate a signal corresponding to an amount of the received light; anda plurality of read-out circuits shared by a predetermined number ofpixels and configured to read out the signal as pixel values, aplurality of pixel value groups by classifying the pixel values for eachof the plurality of read-out circuits; a noise determination unitconfigured to determine whether blinking defect noise occurs in each ofthe plurality of pixel value groups, based on distribution of the pixelvalues of each of the plurality of pixel value groups created by thepixel value group creation unit; and a random noise amount calculationunit configured to calculate a random noise amount of each of theplurality of pixel value groups generated by the pixel value groupcreation unit and calculate a random noise amount of the plurality ofpieces of image data, wherein the noise determination unit is configuredto determine whether the blinking defect noise occurs in each of theplurality of pixel value groups, based on the random noise amount ofeach of the plurality of pixel value groups calculated by the randomnoise amount calculation unit and based on the random noise amount ofthe plurality of pieces of image data calculated by the random noiseamount calculation unit.
 2. The image processing apparatus according toclaim 1, wherein the random noise amount is one of standard deviation,dispersion, distribution range size, a maximum value, and a minimumvalue, of the pixel values.
 3. The image processing apparatus accordingto claim 1, further comprising a feature data calculation unitconfigured to calculate feature data indicating a feature of theblinking defect noise based on the distribution of the pixel values ofeach of the plurality of pixel value groups and based on a model of theblinking defect noise that has been recorded beforehand, wherein thenoise determination unit is configured to determine whether the blinkingdefect noise occurs in each of the plurality of pixel value groups,based on the random noise amount of each of the plurality of pixel valuegroups calculated by the random noise amount calculation unit, based onthe random noise amount of the plurality of pieces of image datacalculated by the random noise amount calculation unit, and based on thefeature data calculated by the feature data calculation unit.
 4. Theimage processing apparatus according to claim 2, wherein the model ofthe blinking defect noise is a mixed distribution model including aplurality of distributions.
 5. The image processing apparatus accordingto claim 3, wherein the feature data include one or more of amplitude ofthe blinking defect noise, frequency of occurrence of the blinkingdefect noise, and frequency of occurrence of the blinking defect noisebelow the amplitude of the blinking defect noise.
 6. The imageprocessing apparatus according to claim 3, wherein the feature datainclude the number of candidates for correction values for correctingthe blinking defect noise.
 7. The image processing apparatus accordingto claim 3, further comprising a noise information acquiring unitconfigured to acquire noise information in which a determination resultobtained by the noise determination unit, positional information of eachof the plurality of read-out circuits, and the feature data areassociated with one another.
 8. The image processing apparatus accordingto claim 7, further comprising a noise correction unit configured tocorrect the blinking defect noise based on the noise informationacquired by the noise information acquiring unit.
 9. The imageprocessing apparatus according to claim 1, wherein the blinking defectnoise is a random telegraph signal noise.
 10. The image processingapparatus according to claim 1, wherein the plurality of pieces of imagedata is captured under a same exposure condition.
 11. The imageprocessing apparatus according to claim 10, wherein the plurality ofpieces of image data is captured in a light-shielded state.
 12. An imageprocessing method comprising: creating, for a plurality of pieces ofimage data generated by an image sensor including: a plurality of pixelsarranged two-dimensionally to receive light from outside and generate asignal corresponding to an amount of the received light; and a pluralityof read-out circuits shared by a predetermined number of pixels andconfigured to read out the signal as pixel values, a plurality of pixelvalue groups by classifying the pixel values for each of the pluralityof read-out circuits; determining whether blinking defect noise occursin each of the plurality of pixel value groups, based on distribution ofthe pixel values of each of the plurality of pixel value groups; andcalculating a random noise amount of each of the plurality of pixelvalue groups generated and calculating a random noise amount of theplurality of pieces of image data, wherein whether the blinking defectnoise occurs in each of the plurality of pixel value groups isdetermined based on the random noise amount of each of the plurality ofpixel value groups calculated and based on the random noise amount ofthe plurality of pieces of image data calculated.
 13. A non-transitorycomputer-readable recording medium with an executable program storedthereon, the program causing an image processing apparatus to execute:creating, for a plurality of pieces of image data generated by an imagesensor including: a plurality of pixels arranged two-dimensionally toreceive light from outside and generate a signal corresponding to anamount of the received light; and a plurality of read-out circuitsshared by a predetermined number of pixels and configured to read outthe signal as pixel values, a plurality of pixel value groups byclassifying the pixel values for each of the plurality of read-outcircuits; determining whether blinking defect noise occurs in each ofthe plurality of pixel value groups, based on distribution of the pixelvalues of each of the plurality of pixel value groups; and calculating arandom noise amount of each of the plurality of pixel value groupsgenerated and calculating a random noise amount of the plurality ofpieces of image data, wherein whether the blinking defect noise occursin each of the plurality of pixel value groups is determined based onthe random noise amount of each of the plurality of pixel value groupscalculated and based on the random noise amount of the plurality ofpieces of image data calculated.